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This essay explores the material imperatives of nineteenth-century statistical ambition, at a time when the nature of paper itself underwent rapid change. It expands the concept of paper technologies to include knowledge about the materials themselves, as well as the infrastructures, techniques, and agreements that made it possible for the right kind of paper to be made available for particular purposes. Taking the production of the forms for the Prussian census as a case in point, it argues that the enumeration effort of Prussian census statisticians went far beyond designing a form, collecting information, noting and compiling figures, or creating tables. Paper knowledge to define and control the material substance of the forms mattered as much as political impetus and statistical methods in getting the census right. [ABSTRACT FROM AUTHOR]

Some states in the U.S. have traditionally received less federal research funding than other states. The National Science Foundation (NSF) created a program in 1979, called the Experimental Program to Stimulate Competitive Research (EPSCoR) to enhance the research competitiveness in such states. While the geographic disparity in federal research funding is well known, the overall impact of federal funding on the research performance of EPSCoR and non-EPSCoR has not been previously studied. In the current study, we compared the combined research productivity of Ph.D. granting institutions in EPSCoR versus the non-EPSCoR states to better understand the scientific impact of federal investments in sponsored research across all states. The research outputs we measured included journal articles, books, conference papers, patents, and citation count in academic literature. Unsurprisingly, results indicated that the non-EPSCoR states received significantly more federal research funding than their EPSCoR counterparts, which correlated with a higher number of faculty members in the non-EPSCoR versus EPSCoR states. Also, in the overall research productivity expressed on a per capita, the non-EPSCoR states fared better than EPSCoR states. However, when the research output was measured based on per $1M investment of federal research funding, EPSCoR states performed significantly better than the non-EPSCoR states in many research productivity indicators, with the notable exception of patents. Together, this study found preliminary evidence that EPSCoR states achieved a high degree of research productivity despite receiving significantly fewer federal research dollars. The limitations and next steps of this study are also discussed. [ABSTRACT FROM AUTHOR]

Topographic maps comprise a rich data source for the processing and analysis of geographic information. However, the management of paper topographic maps is still dominated by an inefficient manual method, and there is no counting equipment for topographic maps that is suitable for narrow map warehouse passages. The existing algorithms relating to paper counting require paper hardness and cannot directly obtain the internal information of topographic maps. To address the above problems, using a map inventory machine transformed from a paddle-type paper counting machine to perform a preliminary count and obtain the video data of the counting progress, an automatic counting algorithm for topographic maps for map management is proposed in this study. First, the periodic feature changes of all the targets and each individual target in the videos are analyzed. Second, a deformable parts model accelerated by fast feature pyramids and by reducing the region of interest is used for the object detection of mechanical wheels. The grayscale variance function is selected to quantify the quality change in the images. Finally, a fusion window counting model is constructed with the adaptive window and fixed window methods. The experimental results show that the average counting accuracy of the model is about 95%, which can verify the initial counting results of the machine and provide high-quality keyframes for the subsequent sheet designation recognition.

Crowd counting from a single image is a challenging task due to perspective distortion and large-scale variation in crowd scenes. Many Researches only focus on local features to create density maps which is not effective in handing the challenges. This paper proposes a novel network named global-to-local adaptive spatial encoder network, which focuses on global features to generate a total structure density map of the population distribution, and then utilizes local features to reconstruct the total structure density map in detail to generate high-quality density map. To capture global features, local information and correlate them, we design a contextual module using different kernels with convolution and transposed convolution. To create a density map from global structure to local detail, two branches are designed, the global distribution branch and the local detail branch. The former aims to capture the population distribution region of interest in terms of global structure, and the latter aims to focus on the local details of each unit. Furthermore, to overcome the problem of pixel-wise loss of MSE, this paper proposes an efficient loss function that focuses on perceiving the possible crowd distribution over the whole image. We also apply a new upsampling mechanism that learns to create high-quality density maps on its own is advisable. The proposed network can capture the characteristics of pedestrian distribution and predict accurate results. It is evaluated on four crowd counting datasets (ShanghaiTech, NWPU, UCF_QNRF, UCF_CC_50), it obtains MAE of 67.1 and MSE, and achieves 108.8 in ShanghaiTech and gets MAE of 139.2 and the best MSE of 217.7 in UCF_CC_50 dataset and so on, and our method shows state-of-the-art on all the datasets. [ABSTRACT FROM AUTHOR]

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model's feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model's detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management. [ABSTRACT FROM AUTHOR]

Topographic maps comprise a rich data source for the processing and analysis of geographic information. However, the management of paper topographic maps is still dominated by an inefficient manual method, and there is no counting equipment for topographic maps that is suitable for narrow map warehouse passages. The existing algorithms relating to paper counting require paper hardness and cannot directly obtain the internal information of topographic maps. To address the above problems, using a map inventory machine transformed from a paddle-type paper counting machine to perform a preliminary count and obtain the video data of the counting progress, an automatic counting algorithm for topographic maps for map management is proposed in this study. First, the periodic feature changes of all the targets and each individual target in the videos are analyzed. Second, a deformable parts model accelerated by fast feature pyramids and by reducing the region of interest is used for the object detection of mechanical wheels. The grayscale variance function is selected to quantify the quality change in the images. Finally, a fusion window counting model is constructed with the adaptive window and fixed window methods. The experimental results show that the average counting accuracy of the model is about 95%, which can verify the initial counting results of the machine and provide high-quality keyframes for the subsequent sheet designation recognition. [ABSTRACT FROM AUTHOR]

Bipartite graphs widely exist in real-world scenarios and model binary relations like host-website, author-paper, and user-product. In bipartite graphs, a butterfly (i.e., $2\times 2$ 2 × 2 bi-clique) is the smallest non-trivial cohesive structure and plays an important role in applications such as anomaly detection. Considerable efforts focus on counting butterflies in static bipartite graphs. However, they suffer from high time and space complexity when the bipartite graph of interest is given as a stream of edges. Although there are methods for approximately counting butterflies from bipartite graph streams, they suffer from either low accuracy or high time complexity. Therefore, it is still a challenge to accurately estimate butterfly counts from bipartite graph streams in a short time. To address this issue, we develop novel algorithms by exploiting the bipartite nature, which subtly integrates sampling and sketching techniques. We provide accurate estimators for butterfly counts and derive simple yet exact formulas for bounding their errors. We also conduct extensive experiments on a variety of real-world large bipartite graphs. Experimental results demonstrate that our algorithms are up to 20.0 times more accurate and up to 286.3 times faster than state-of-the-art methods under the same memory usage. [ABSTRACT FROM AUTHOR]

Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry. [ABSTRACT FROM AUTHOR]

While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries. While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, the authors develop models that untangle the network effects of productivity and prominence of individual scientists from their collaboration networks. [ABSTRACT FROM AUTHOR]

This paper critically examines the possibilities of using the British Museum's collection database as a research tool to examine acquisition history. It publishes initial findings from the author's research into the history of the collection through a quantitative analysis of collection data. Rather than focusing on individual collectors, collections or areas of the collection, it takes a high-level comprehensive view of the current body of material, across time, departments and material. The paper asks three questions of the database: when did objects enter the museum's collection, from whom and how? Through a numerical analysis, acquisition patterns are revealed which both challenge and augment our understanding of the history of the collection. [ABSTRACT FROM AUTHOR]

In recent years, real-time vehicle congestion detection has become a hot research topic in the field of transportation due to the frequent occurrence of highway traffic jams. Vehicle congestion detection generally adopts a vehicle counting algorithm based on object detection, but it is not effective in scenarios with large changes in vehicle scale, dense vehicles, background clutter, and severe occlusion. A vehicle object counting network based on a feature pyramid split attention mechanism is proposed for accurate vehicle counting and the generation of high-quality vehicle density maps in highly congested scenarios. The network extracts rich contextual features by using blocks at different scales, and then obtains a multi-scale feature mapping in the channel direction using kernel convolution of different sizes, and uses the channel attention module at different scales separately to allow the network to focus on features at different scales to obtain an attention vector in the channel direction to reduce mis-estimation of background information. Experiments on the vehicle datasets TRANCOS, CARPK, and HS-Vehicle show that the proposed method outperforms most existing counting methods based on detection or density estimation. The relative improvement in MAE metrics is 90.5% for the CARPK dataset compared to Fast R-CNN and 73.0% for the HS-Vehicle dataset compared to CSRNet. In addition, the method is also extended to count other objects, such as pedestrians in the ShanghaiTech dataset, and the proposed method effectively reduces the misrecognition rate and achieves higher counting performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Let Fq be the finite field of order q where q = pk, k is a positive integer and p is an odd prime. Let F*q represent the nonzero elements of Fq. For f(x1,···,xn) ∊ Fq[x1,···,xn], we used N(f(x1,···,xn) = 0) to denote the number of Fq-rational points of the affine hypersurface f(x1,···,xn) = 0. In 2020, Zhao et al. obtained the explicit formulae for N(x41 + x42 = c), N(x41 + x42+ x43 = c) and N(41+ x42+ x43+ x44 = c) over Fq, with c ∊ F*q. In this paper, by using Jacobi sums and an analog of the Hasse-Davenport theorem, we arrived at explicit formulae for N(a1x41 + a2x42 = c) and N(b1x41 + b2x42 + b3x43 = c) with ai, bj ∊ F*q(1 ≤ i ≤ 2, 1 ≤ j ≤ 3) and c ∊ Fq. Furthermore, by using the reduction formula for Jacobi sums, the number of rational points of the quartic diagonal hypersurface a1x41 + a2x42 +···+ anx4n = c of n ≥ 4 variables with ai ∊ F*q (1 ≤ i ≤ n), c ∊ Fq and p = 1(mod 4), can also be deduced. These extended and improved earlier results. [ABSTRACT FROM AUTHOR]

Traffic analysis is one of the crucial tasks of intelligent transport system that utilizes deep learning for range of purposes. Many tasks, such as vehicle recognition, vehicle counting, traffic violation monitoring, vehicle speed monitoring, vehicle density and so on, can be accomplished by using cameras installed in strategic locations along roads. In this paper powerful deep learning techniques such as (Yolov5, Mask RCNN, SSD) and state-of-the-art object tracking algorithm known as DeepSORT was used to perform real time vehicle detection and counting in a video. A new highway vehicle detection dataset with overall of 32,265, instances of four vehicle classes named: bus, car, motorbike, truck was created in this paper and utilized for training vehicle detection and counting system. Result shows that average counting accuracy by using Yolov5 combined DeepSORT reaches to 95% while reaches to 91% by using Mask R-CNN combined DeepSORT and 84% by using SSD combined DeepSORT in hard environment. From the experimental work, counting accuracy by using Yolov5 outperforms other two deep learning techniques. [ABSTRACT FROM AUTHOR]

Event sequences are common types of data. Several episode mining algorithms have been developed to find episodes (subsequences of events) that appear frequently in an event sequence, with the aim of discovering useful knowledge for decision-making and predictions. However, most of these algorithms can only process simple event sequences (where, at most, one event occurs at each timestamp). In contrast, in many real-life applications, multiple events may occur at the same timestamp, resulting in complex event sequences. Moreover, numerous episode mining algorithms overestimate the frequency of episodes by counting the same events multiple times. As a solution, some algorithms have been designed to count only non-overlapping occurrences. Yet, it can be argued that this definition is too strict and discards many important events. To address these limitations, this paper presents an algorithm named EMDO (Episode Mining under Distinct Occurrences (EMDO) to find frequent episodes in a complex sequence by counting distinct occurrences. The proposed concept of distinct occurrences ensures that each event is not counted more than once but allows distinct occurrences to overlap. A second algorithm, called EMDO-P, is also presented in this paper to derive strong episode rules in event sequences from episodes found by EMDO. To the best of our knowledge, this is the first study on mining frequent episodes using a frequency definition based on distinct occurrences. The experimental results confirm that the proposed algorithms are efficient. [ABSTRACT FROM AUTHOR]

In this paper, the asmptotics is considered for the distribution tail of a randomly stopped sum Sν = X1 + ··· + Xν of independent identically distributed consistently varying random variables with zero mean, where ν is a counting random variable independent of {X1,X2, . . .}. The conditions are provided for the relation P(Sν > x) ∼ Eν P(X1 > x) to hold, as x →∞, involving the finiteness of E|X1|. The result improves that of Olvera-Cravioto [14], where the finiteness of a moment E|X1|r for some r > 1 was assumed. [ABSTRACT FROM AUTHOR]

Crowd counting aims to estimate the number, density, and distribution of crowds in an image. The current mainstream approach, based on CNN, has been highly successful. However, CNN is not without its flaws. Its limited receptive field hampers the modeling of global contextual information, and it struggles to effectively handle scale variation and background complexity. In this paper, we propose a Multi-scale Hybrid Attention Network called MHANet to solve crowd counting challenges more effectively. To address the issue of scale variation, we have developed a Multi-scale Aware Module (MAM) that incorporates multiple sets of dilated convolutions with varying dilation rates. The MAM significantly improves the network's ability to extract information at multiple scales. To tackle the problem of background complexity, we have introduced a Hybrid Attention Module (HAM) that combines spatial attention and channel attention. The HAM effectively directs attention to the crowd region while suppressing background interference, resulting in more accurate counting. MHANet has been extensively experimented on four benchmark datasets and compared against state-of-the-art algorithms. It consistently achieves superior performance in terms of the MAE evaluation metric. MHANet outperforms the current state-of-the-art methods by margins of 1.9%, 5.4%, 0.4%, and 0.8% on the ShanghaiTech Part_A, ShanghaiTech Part_B, UCF-QNRF, and UCF_CC_50 datasets, respectively. Furthermore, a series of ablation experiments targeting MAM and HAM were conducted in this paper, and the experimental results fully demonstrate that MAM and HAM can effectively address the challenges of scale variation and background complexity, ultimately enhancing the accuracy and robustness of the network. [ABSTRACT FROM AUTHOR]

Estimating the population in a given scene is a process known as crowd counting. The field has recently garnered significant attention, and many innovative methods have emerged. However, intense scale variations and background interference make crowd counting in realistic scenes always challenging. To address these in this paper, a multi-level attention network with multi-scale feature fusion named MLANet is proposed. The network consists of three sections: a multi-level base feature extraction front-end network, a centralized dilated multi-scale feature fusion mid-end network with a global attention module, and a back-end network for the generation of density maps. By incorporating a flexible attention module and multi-scale features, the method can accurately capture crowd information at different scales and achieve accurate counting results. We evaluated the method on four public datasets (UCF_CC_50, ShanghaiTech, WorldExpo'10, and Beijing BRT), and the experimental results demonstrate a significant reduction in counting error when compared with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study counting perfect matchings in linear chain graphs, focusing on identically colored and alternatingly colored odd faces, using recurrence relations. Our primary objective is to derive explicit formulas for the numbers of perfect matchings in linear chain graphs with identically colored odd faces. Furthermore, we establish a relationship between the numbers of perfect matchings in linear chain graphs with identically colored odd faces and strip snake chain graphs. This relationship provides us with an alternative way of validating the numbers of perfect matchings in linear chain graphs with the same colored odd faces. [ABSTRACT FROM AUTHOR]

The Zhang–Zhang polynomial of a benzenoid system is a well-known counting polynomial that was introduced in 1996. It was designed to enumerate Clar covers, which are spanning subgraphs with only hexagons and edges as connected components. In 2018, the generalized Zhang–Zhang polynomial of two variables was defined such that it also takes into account 10-cycles of a benzenoid system. The aim of this paper is to introduce and study a new variation of the Zhang–Zhang polynomial for phenylenes, which are important molecular graphs composed of 6-membered and 4-membered rings. In our case, Clar covers can contain 4-cycles, 6-cycles, 8-cycles, and edges. Since this new polynomial has three variables, we call it the multivariable Zhang–Zhang (MZZ) polynomial. In the main part of the paper, some recursive formulas for calculating the MZZ polynomial from subgraphs of a given phenylene are developed and an algorithm for phenylene chains is deduced. Interestingly, computing the MZZ polynomial of a phenylene chain requires some techniques that are different to those used to calculate the (generalized) Zhang–Zhang polynomial of benzenoid chains. Finally, we prove a result that enables us to find the MZZ polynomial of a phenylene with branched hexagons. [ABSTRACT FROM AUTHOR]

This paper considers a deterministic susceptible-infected-susceptible (SIS) networked bivirus epidemic model (termed the bivirus model for short), in which two competing viruses spread through a set of populations (nodes) connected by two graphs, which may be different if the two viruses have different transmission pathways. The networked dynamics can give rise to complex equilibria patterns, and most current results identify conditions on the model parameters for convergence to the healthy equilibrium (where both viruses are extinct) or a boundary equilibrium (where one virus is endemic and the other is extinct). However, there are only limited results on coexistence equilibria (where both viruses are endemic). This paper establishes a set of "counting" results which provide lower bounds on the number of coexistence equilibria and, perhaps more importantly, establish properties on the local stability/instability properties of these equilibria. In order to do this, we employ the Poincaré--Hopf theorem but with significant modifications to overcome several challenges arising from the bivirus system model, such as the fact that the system dynamics do not evolve on a manifold in the typical sense required to apply Poincaré--Hopf theory. Subsequently, Morse inequalities are used to tighten the counting results, under the reasonable assumption that the bivirus system is a Morse--Smale dynamical system. Numerical examples are provided which demonstrate the presence of multiple attractor equilibria and multiple coexistence equilibria. [ABSTRACT FROM AUTHOR]

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting. [ABSTRACT FROM AUTHOR]

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The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and addresses unreasonable ID assignment caused by unreliable feature matching in the context of region division. Through the macro analysis of the scene, the authors define the region (called the transition region) where the number of objects can increase or decrease and require that all ID assignments for new objects and ID deletions for existing objects take place only in the transition region. Because the actual number of objects in the non-transition region is constant, they rematch unmatched objects with existing IDs in the region (called ID relocation) because changes in object ID are caused by feature matching failure. In this paper, the authors create algorithms for dynamically generating transition regions, detecting object increases and decreases, and relocating object IDs. Experimental results show that the method effectively improves the accuracy of video object counting. [ABSTRACT FROM AUTHOR]

This paper proposes a generalized form of Apollonian networks and derives their closed expression for the numbers of spanning trees. The n-edges Apollonian networks are transformed into n-order weighted wheels using the electrically equivalent transformations and weighted generating function rules. The numbers of spanning trees in the weighted fans are then calculated to obtain the counting formula of spanning trees in the weighted wheels, thereby deriving the exact expression for spanning trees of the generalized Apollonian networks. [ABSTRACT FROM AUTHOR]

Prevailing crowd counting approaches primarily rely on density map regression methods. Despite wonderful progress, significant scale variations and complex background interference within the same image remain challenges. To address these issues, in this paper we propose a novel DETR-based crowd counting framework called Crowd Counting DETR (CC-DETR), which aims to extend the state-of-the-art DETR object detection framework to the crowd counting task. In CC-DETR, a DETR-like encoder–decoder structure (Hybrid Context DETR, i.e., HCDETR) is proposed to tackle complex visual information by fusing features from hybrid semantic levels through a transformer. In addition, we design a Coordinate Dilated Convolution Module (CDCM) to effectively employ position-sensitive context information in different scales. Extensive experiments on three challenging crowd counting datasets (ShanghaiTech, UCF-QNRF, and NWPU) demonstrate that our model is effective and competitive when compared against SOTA crowd counting models. [ABSTRACT FROM AUTHOR]

The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings. [ABSTRACT FROM AUTHOR]

Let S = {z C : P(z) = zn+azn-1+b = 0}, where a, b C be nonzero constants satisfying b an = (-1)n(n - 1)n-1 nn . The uniqueness of meromorphic functions sharing S counting multiplicity(resp. with weight 2) has been studied by Yi ([18]) (resp. Lahiri, Banerjee ([12])). In this paper, we consider the uniqueness of meromorphic functions sharing S ignoring multiplicity. We first obtain the analog of Yi's Theorem 2 ([18]). Next, we show that S is a unique range set for the class of meromorphic functions ignoring multiplicity of higher multiplicities of either zeros or poles, which different from S. Mallick - D. Sarkar's ([13]). We discuss some applications of the main result. Our results are inspired by a work of Yi ([18]) and Khoai ([11]). [ABSTRACT FROM AUTHOR]

In rice cultivation and breeding, obtaining accurate information on the quantity and spatial distribution of rice plants is crucial. However, traditional field sampling methods can only provide rough estimates of the plant count and fail to capture precise plant locations. To address these problems, this paper proposes P2PNet-EFF for the counting and localization of rice plants. Firstly, through the introduction of the enhanced feature fusion (EFF), the model improves its ability to integrate deep semantic information while preserving shallow spatial details. This allows the model to holistically analyze the morphology of plants rather than focusing solely on their central points, substantially reducing errors caused by leaf overlap. Secondly, by integrating efficient multi-scale attention (EMA) into the backbone, the model enhances its feature extraction capabilities and suppresses interference from similar backgrounds. Finally, to evaluate the effectiveness of the P2PNet-EFF method, we introduce the URCAL dataset for rice counting and localization, gathered using UAV. This dataset consists of 365 high-resolution images and 173,352 point annotations. Experimental results on the URCAL demonstrate that the proposed method achieves a 34.87% reduction in MAE and a 28.19% reduction in RMSE compared to the original P2PNet while increasing R 2 by 3.03%. Furthermore, we conducted extensive experiments on three frequently used plant counting datasets. The results demonstrate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Since the emergence of big data, the popularity of deep learning models has increased and they are being implemented in a wide range of applications, including people detection and counting in congested environments. Detecting and counting people for human behavior analysis in retail stores is a challenging research problem due to the congested and crowded environment. This paper proposes a deep learning approach for detecting and counting people in the presence of occlusions and illuminance variation in a crowded retail environment, utilizing deep CNNs (DCNNs) for semantic segmentation of top-view depth visual data. Semantic segmentation has been implemented using (DCNNs) in recent years since it is a powerful approach. The objective of this paper is to design a novel architecture that consists of an encoder–decoder architecture. We were motivated to use transfer learning to solve the problem of insufficient training data. We used ResNet50 for the encoder, and we built the decoder part as a novel contribution. Our model was trained and evaluated on the TVHeads dataset and the people counting dataset (PCDS) that are available for research purposes. It consists of depth data of people captured from a top-view RGB-D sensor. The segmentation results indicate high accuracy and demonstrate that the proposed model is robust and accurate. [ABSTRACT FROM AUTHOR]

Nowadays, fish farming plays an essential role in aquaculture. In the process of fry sales, the traditional manual counting method takes a great deal of time. Because the fish are constantly moving in the water, they are difficult to count and are likely to be unnecessarily damaged. With the rise of computer vision, fry counting based on image processing is also developing. It has the advantages of having high accuracy and fast processing speed. The primary counting process is divided into detection, adhesion segmentation, and fry counting. First, we improved the maximum interclass variance threshold selection method, and we extracted the histogram features under different channels to detect the fry; then, a matching segmentation method based on concave and skeleton crossing points was used to segment the adherent fry to improve the counting accuracy, the concaves were detected by CNN. We tested the method on real fry data sets, and the average accuracy reached 97%. Our approach had better accuracy than any existing current fish counting methods. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Following a recent publication, in this paper we count the number of independent operators at arbitrary mass dimension in N = 1 supersymmetric gauge theories and derive their field and derivative content. This work uses Hilbert series machinery and extends a technique from our previous work on handling integration by parts redundancies to vector superfields. The method proposed here can be applied to both abelian and non-abelian gauge theories and for any set of (chiral/antichiral) matter fields. We work through detailed steps for the abelian case with single flavor chiral superfield at mass dimension eight, and provide other examples in the appendices. [ABSTRACT FROM AUTHOR]

Background. In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods. In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN "mid-term" fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results. MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion. MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets. [ABSTRACT FROM AUTHOR]

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, A P of 71.4% and A P 50 of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting. [ABSTRACT FROM AUTHOR]

As deep learning continues to advance, object detection technology holds potential and promising prospects in the recognition of cylindrical objects' quantity, such as in industries like timber processing, construction, and pipeline engineering. The traditional manual counting methods have lower efficiency, a higher error rate, and demand a greater amount of manpower. The introduction of object detection technology can effectively address these issues, enhance work efficiency, and reduce labor costs. Therefore, this research paper introduces a novel variant of the YOLOv5s algorithm, called YOLOv5-COC, specifically designed to tackle the task of counting cylindrical objects. This paper makes the following significant contributions: Firstly, introducing the utilization of data augmentation techniques to augment the dataset, thereby enhancing the generalization ability of the model. Secondly, the K-means + ⁣ + algorithm is employed as an alternative to the conventional K-means algorithm in order to enhance the initialization of anchor boxes. Thirdly, introduce distinct methodologies, including the incorporation of a coordinated attention mechanism, the amalgamation of the Bidirectional Feature Pyramid Network (BiFPN), and the substitution of the loss function, in order to further refine the model and enhance its recognition precision. Finally, employ ablation experiments to assess the optimization outcomes of the aforementioned methodologies. The experimental results reveal that the YOLOv5-COC model proposed in this study attains an mAP of 98.7%, operates at a frame rate of 60 FPS, attains a Precision of 98.3%, and boasts a Recall of 99.1%. The mAP@0.5:0.95 stands at 72.4%. In comparison to the original YOLOv5s model, the mAP value exhibits an improvement of 1.3%, the FPS experiences a remarkable surge of 27.7%, detection accuracy elevates by 1%, the recall rate advances by 1%, and the mAP@0.5:0.95 escalates by 3.5%. In summary, the YOLOv5-COC model demonstrates a sufficiently high level of accuracy in object detection tasks, mitigating instances of both false negatives and false positives. It efficiently accomplishes the task of object detection. [ABSTRACT FROM AUTHOR]

The paper addresses the challenging problem of counting objects or entities in an image without relying on specific category information, known as class-agnostic counting (CAC). While CAC is an innovative solution, it faces difficulties due to intra-class diversity, which is influenced by variations in shape, color, and scale. To overcome this challenge, the paper proposes two modules: the Feature Augmentation Module (FAM) and the Similarity Comparison Module (SCM). FAM enhances the feature representation by generating additional samples based on input examples, thereby increasing the number of samples in the feature space. SCM dynamically evaluates the self-similarity of instances and emphasizes core patterns to make the features more robust against intra-class variations. The model is guided to focus on regions of the query image that are similar to the exemplar image, ensuring clear differentiation between different items. Experimental results on various benchmarks and training environments demonstrate the exceptional performance of the proposed approach, outperforming current state-of-the-art techniques by a substantial margin. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) estimation is an essential element that serves as the basic data for the battery management system (BMS). If a BMS problem occurs due to an SOC estimation error, a problem may occur in the entire battery power system. In this paper, we propose an algorithm that simultaneously applies the Coulomb Counting Method (CCM) and Extended Kalman filter (EKF) for SOC estimation. The most common CCM is useful when the capacity and initial SOC of a battery are accurate, but it is disadvantageous when the initial conditions are inaccurate, an error may occur and the accumulation error increases over time. To solve this problem, we propose a method of simultaneously applying EKF to the initial SOC estimate. For the evaluation of the proposed algorithm, the experiment was conducted using Simulink, and the experimental results confirmed that the proposed method improved the estimation error compared to the existing SOC estimation method. [ABSTRACT FROM AUTHOR]

Teacher quality in primary schools in South Africa is a serious concern, and how best to prepare teachers in initial teacher education—with regard to mathematics—remains an open and vexing question. There is a confluence of challenges facing the mathematics education community in South Africa: poor conceptual understanding of mathematics amongst teachers; poor attainment in Primary Teacher Education mathematics tests at both first and fourth year levels; and few initial teacher education programmes having evidence of improving knowledge for teaching mathematics. In addition, few lecturers in initial teacher education make explicit the theoretical tenets informing their course design for mathematics. In response to the latter challenge, this paper makes explicit the design choices made in the Emergent Number Sense first year mathematics module in Bachelor of Education degrees, used across six universities in South Africa. It then theorises the choice to commence the B.Ed mathematics programmes with two pathways into number knowledge: (1) a counting pathway, making use of discrete objects or events which can be quantified by counting in ones; and (2) a measurement pathway, making use of continuous extents which can be quantified by introducing a unit of measurement. Each has a distinct metaphor, strategy for the reification of the 10 and supporting representation. This offers the lecturers using the course in their own design trials, and initial teacher educators beyond the Maths4Primary Teachers collective, a conceptual framework for this design choice, which can now be critiqued and further improved. [ABSTRACT FROM AUTHOR]

Simple Summary: In large-scale chicken farming, monitoring flock density can help optimize feeding management and improve animal welfare. Traditional manual counting methods are not only prone to errors and omissions but also can cause harm to chickens. In this paper, a counting model for dense chicken flocks is proposed. The method can effectively estimate the number of chickens in real dense scenes. With the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a dense flock of chickens counting network (DFCCNet) is proposed based on density map regression, where features from different levels are merged using feature fusion to obtain more information for distinguishing chickens from the background, resulting in more stable counting results. Multi-scaling is used to detect and count chickens at various scales, which can improve the counting accuracy and ensure stable performance for chickens of different sizes. Feature convolution kernels are adopted to convolve feature maps, which can extract more accurate target information, reduce the impact of occlusion, and achieve more reliable and precise results. A dataset of dense flocks of chickens (namely Dense-Chicken) has been collected and constructed, which contains 600 images of 99,916 chickens, with labeled points and boxes. It can be accessed by researchers as benchmark data. The proposed method was compared with some state-of-the-art algorithms, to validate its effectiveness. With its robustness being verified by counting in three kinds of density situations, with the mean absolute error being 4.26, 9.85, and 19.17, respectively, and a speed of 16.15 FPS. DFCCNet provides an automatic and fast approach to counting chickens in a dense farming environment. It can be easily embedded into handheld devices for application in agricultural engineering. [ABSTRACT FROM AUTHOR]

The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video. [ABSTRACT FROM AUTHOR]

Let G be a graph. A dissociation set of G is a subset of vertices that induces a subgraph with vertex degree at most 1. The dissociation polynomial of G is D G (λ) = ∑ D ∈ D (G) λ | D | , where D (G) is the set of all dissociation sets of G . In this paper, we prove that for any cubic graph G and any λ ∈ (0 , 1 ] , 1 | V (G) | ln ⁡ D G (λ) ≤ 1 4 ln ⁡ D K 4 (λ) with equality if and only if G is a disjoint union of copies of the complete graph K 4 . When λ = 1 , the value of D G (λ) is exactly the number of dissociation sets of G . Hence, for any cubic graph G on n vertices, | D (G) | ≤ | D (K 4) | n / 4 = 11 n / 4. Let be a graph. A dissociation set of is a subset of vertices that induces a subgraph with vertex degree at most 1. The dissociation polynomial of is , where is the set of all dissociation sets of . In this paper, we prove that for any cubic graph and any , with equality if and only if is a disjoint union of copies of the complete graph . When , the value of is exactly the number of dissociation sets of . Hence, for any cubic graph on vertices, [ABSTRACT FROM AUTHOR]

Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved. [ABSTRACT FROM AUTHOR]

Accurate identification of soybean pods is an important prerequisite for obtaining phenotypic traits such as effective pod number and seed number per plant. However, traditional image-processing methods are sensitive to light intensity, and feature-extraction methods are complex and unstable, which are not suitable for pod multi-classification tasks. In the context of smart agriculture, many experts and scholars use deep learning algorithm methods to obtain the phenotype of soybean pods, but empty pods and aborted seeds are often ignored in pod classification, resulting in certain errors in counting results. Therefore, a new classification method based on the number of effective and abortive seeds in soybean pods is proposed in this paper, and the non-maximum suppression parameters are adjusted. Finally, the method is verified. The results show that our classification counting method can effectively reduce the errors in pod and seed counting. At the same time, this paper designs a pod dataset based on multi-device capture, in which the training dataset after data augmentation has a total of 3216 images, and the distortion image test dataset, the high-density pods image test dataset, and the low-pixel image test dataset include 90 images, respectively. Finally, four object-detection models, Faster R-CNN, YOLOv3, YOLOv4, and YOLOX, are trained on the training dataset, and the recognition performance on the three test datasets is compared to select the best model. Among them, YOLOX has the best comprehensive performance, with a mean average accuracy (mAP) of 98.24%, 91.80%, and 90.27%, respectively. Experimental results show that our algorithm can quickly and accurately achieve the high-throughput counting of pods and seeds, and improve the efficiency of indoor seed testing of soybeans. [ABSTRACT FROM AUTHOR]

This paper considers the structure consisting of the set of all words over a given alphabet together with the subword relation, regular predicates, and constants for every word. We are interested in the counting extension of first-order logic by threshold counting quantifiers. The main result shows that the two-variable fragment of this logic can be decided in twofold exponential alternating time with linearly many alternations (and therefore in particular in twofold exponential space as announced in the conference version (Kuske and Schwarz, in: MFCS'20, Leibniz International Proceedings in Informatics (LIPIcs) vol. 170, pp 56:1–56:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020) of this paper) provided the regular predicates are restricted to piecewise testable ones. This result improves prior insights by Karandikar and Schnoebelen by extending the logic and saving one exponent in the space bound. Its proof consists of two main parts: First, we provide a quantifier elimination procedure that results in a formula with constants of bounded length (this generalises the procedure by Karandikar and Schnoebelen for first-order logic). From this, it follows that quantification in formulas can be restricted to words of bounded length, i.e., the second part of the proof is an adaptation of the method by Ferrante and Rackoff to counting logic and deviates significantly from the path of reasoning by Karandikar and Schnoebelen. [ABSTRACT FROM AUTHOR]

The Troubles have spread heartbreak and misery across Northern Ireland, mainland Britain, and Europe for 50 years, claiming the lives of thousands and victimizing tens of thousands. With each death, physical, and psychological injury there are stories of a father, son, brother, sister, daughter, and mother. Human security encapsulates the lives of these people and examines their experiences front and center, and for the first time this sentiment will be adopted in an academic paper to analyze casualty counting in Northern Ireland. This study demonstrates that the Northern Irish Troubles are not just suitable for casualty recording but are also in desperate need of it, as current casualty recording facilities are inadequate in tackling victimization and providing closure to grief-stricken families. This paper analyzes the theoretical aspects of casualty counting and inspects human rights laws in Northern Ireland, seeking to remedy current issues of victim hierarchies in database inclusions, incomplete timelines, and limited accessibility to records, in order to provide Troubles victims with the recognition and answers they yearn for. The overarching theme of this study is that effective casualty recording is a steppingstone to bridging the community divide in Northern Ireland, bringing accountability and ultimately ending the war. [ABSTRACT FROM AUTHOR]

As a dense instance segmentation problem, rebar counting in a complex environment such as rebar yard and rebar transpotation has received significant attention in both academic and industrial contexts. Traditional counting approaches, such as manual counting and machine vision-based algorithms, are often inefficient or inaccurate since rebars with varied sizes and shapes are stacked overlapping, rebar image is not clear for complex light condition such as dawn, night and strong light, and other environmental noises exist in rebar image; thus, they no longer fulfil the requirements of modern automation. This paper proposes MaskID, an innovative counting method based on deep learning and heuristic strategies. First, an improved version of the Mask region-based convolutional neural network (Mask R-CNN) was designed to obtain the segmentation results through splitting and rescaling so as to capture more detail in a large-scale rebar image. Then, a series of intelligent denoising strategies corresponding to aspect ratio of recognized box, overlapping recognized objects, object distribution and environmental noise, were applied to improve the segmentation results. The performance of the proposed method was evaluated on open-competition and test-platform datasets. The F1-score was found to be over 0.99 on all datasets. The experimental results demonstrate that the proposed method is effective for dense rebar counting and significantly outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Paper aims: The objectives of this paper are: (i) to analyze the impacts of Inventory Record Inaccuracy (IRI) on picking productivity (PP), lost sales (LS), and warehouse capacity utilization (WCU) for different warehouses; (ii) verify if the Cycle Counting (CC) implementation is sufficient to reduce IRI and (iii) the number of CC operators to maintain warehouse performance. Originality: A causal loop diagram in detail of the IRI and CC variable's effects in receiving and picking processes is developed. IRI is separated into ghost and hidden inventory to represent the actual effects in WCU, LS, and PP. Research method: A dynamic system model is built. Simulations are performed for the five categories of warehouse performance with different numbers of CC operators. Main findings: IRI impacts in all warehouses simulated, so managers should monitor IRI besides employee productivity. CC can reduce IRI in all types of warehouses. The number of CC operators to be implemented is different for each warehouse. Implications for theory and practice: Several implications for theory and practice are discussed. For example, IRI impacts more ADV and BIC warehouses without CC implementation. In TYP, ADV, and BIC warehouses CC is sufficient to reduce IRI. In MO and DIS warehouses, other improvements are needed. [ABSTRACT FROM AUTHOR]

Recently, state-of-the-art crowd counting methods have focused more on predicting a density map and then obtaining the final aggregated count. In 2018, a typical density map-based network for congested scene recognition called CSRNet was proposed, and it achieved better crowd counting performance than previous methods with a simple architecture. It utilizes the first 10 layers from VGG-16 as the front end and deploys dilated convolutional layers as the back-end to generate high-quality density maps. CSRNet has been demonstrated on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the World Expo'10 dataset, and the UCSD dataset) and delivered great performance. To obtain better performance, in this paper, we propose a small network as a new component that generates a counting residual estimation, and we combine our component with CSRNet. We demonstrate this combined network on three datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, and the World Expo'10 dataset) and compare the results with those of CSRNet. The results show that our method has significantly improved the results of CSRNet. Through a series of experiments, such as ablation experiments and control experiments, we demonstrate the effectiveness of our method. In the future, we will apply our method to other networks to achieve better results. [ABSTRACT FROM AUTHOR]