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In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

Because of the limitations of current PCB detection methods, such as low detection accuracy and high rate of false and missed detection, a PCB defect detection algorithm based on improved RetinaNet is proposed, that is, RetinaNet-line. Firstly, the algorithm uses PVT v2B2-Hi as the backbone network of the whole model, which can not only solve the deep feature redundancy caused by the residual network in RetinaNet but also improve the feature extraction ability of the network. Then, the CARAFE up-sampling module is used to enrich the semantic features of defective targets and promote the discrimination ability of the algorithm to defective targets. Finally, HSV color space is used to process the input feature image, and the color distribution is expanded by random perturbation in H, S, and V channels, with the data enhancement completed. In this paper, experiments are conducted on the publicly shared PCB defect detection dataset from the Intelligent Open Laboratory of Peking University, and the results show that the improved algorithm achieves 94.2% in the mAP index, which is better than other networks. [ABSTRACT FROM AUTHOR]

To address the issues of poor detection accuracy and the large number of target detection model parameters in existing AGV monocular vision location detection algorithms, this paper presents an AGV vision location method based on Gaussian saliency heuristic. The proposed method introduces a fast and accurate AGV visual detection network called GAGV-net. In the GAGV-net network, a Gaussian saliency feature extraction module is designed to enhance the network's feature extraction capability, thereby reducing the required output for model fitting. To improve the accuracy of target detection, a joint multi-scale classification and detection task header are designed at the stage of target frame regression to classification. This header utilizes target features of different scales, thereby enhancing the accuracy of target detection. Experimental results demonstrate a 12% improvement in detection accuracy and a 27.38 FPS increase in detection speed compared to existing detection methods. Moreover, the proposed detection network significantly reduces the model's size, enhances the network model's deployability on AGVs, and greatly improves detection accuracy. [ABSTRACT FROM AUTHOR]

Deep learning shows great potential in low-light image enhancement, which can improve image brightness and contrast while keeping image natural. However, due to the lack of prior knowledge extracted manually or excessive amplification of noise, these methods result in poor quality of enhanced images. To solve these challenging problems, this paper proposes a dual-branch grayscale image enhancement network based on dense residual and attention mechanism. Low-light grayscale image is used as input. Firstly, features that fuse deep and shallow information are extracted through dense residual convolution branch network; secondly, texture features are extracted through U-Net branch network combined with attention mechanism; then the extracted features are integrated, and finally luminance is adjusted by a brightness adjustment module to output an enhanced grayscale image. In addition, a joint loss function is designed to measure the network training loss from brightness, texture, contrast and noise. A large number of quantitative and qualitative experiments on LOL and VE-LOL datasets show that the proposed method improves Peak Signal to Noise Ratio Index and Structural Similarity Index by 19.65 - 59.76% and 5.61 - 85.53%, respectively, compared with EnlightenGAN, KinD++, RUAS, LLFlow, etc. The proposed method is superior to the most famous methods, thanks to the deep feature extraction and fusion capability of dense residual convolutional network and texture extraction capability of U-Net network combined with attention mechanism. [ABSTRACT FROM AUTHOR]

Under the condition of low signal-to-noise ratio, for the problem of insufficient speech feature extraction and speech enhancement effect of the traditional neural network, this paper is based on empirical mode decomposition (EMD), temporal convolutional network (TCN), and gated convolution recurrent neural network (GCRN), while combining with feature fusion module (FFM), the adaptive mean median-empirical mode decomposition-multilayer gated feature fusion module convolutional recurrent neural networks (ME-MGFCRNs) for speech enhancement modeling. The network model uses a split-frequency learning strategy to learn low-frequency features and high-frequency features, i.e., the TCN and MGFCRN networks are used to obtain low-frequency and high-frequency features, and FFM processes the two sets of features to achieve speech enhancement in the form of feature mapping. The model proposed in this paper performs ablation and comparison experiments on the dataset to evaluate the enhancement effect of speech using PESQ, FwSegSNR, and STOI metrics. The research shows that under different noise environments and SNR conditions, the model proposed in this paper improves compared with other baseline models, especially under the low SNR condition of − 5 dB, FwSegSNR and PESQ improve by more than 0.86 dB and 0.02 compared with other baseline models. [ABSTRACT FROM AUTHOR]

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages. [ABSTRACT FROM AUTHOR]

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology's reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. [ABSTRACT FROM AUTHOR]

What are the best methods of capturing thematic similarity between literary texts? Knowing the answer to this question would be useful for automatic clustering of book genres, or any other thematic grouping. This paper compares a variety of algorithms for unsupervised learning of thematic similarities between texts, which we call "computational thematics". These algorithms belong to three steps of analysis: text pre-processing, extraction of text features, and measuring distances between the lists of features. Each of these steps includes a variety of options. We test all the possible combinations of these options. Every combination of algorithms is given a task to cluster a corpus of books belonging to four pre-tagged genres of fiction. This clustering is then validated against the "ground truth" genre labels. Such comparison of algorithms allows us to learn the best and the worst combinations for computational thematic analysis. To illustrate the difference between the best and the worst methods, we then cluster 5000 random novels from the HathiTrust corpus of fiction. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Recently, video transmission is going through many failures because of the limited size of the top-notch technique for storing large volume videos. Thus, video compression (VC) techniques are introduced, which try to eradicate various sorts of redundancies within or betwixt video sequences. However, the VC often falls short to maintain a good quality of compression if motion discontinuities are present in the video frames (VF). To trounce this challenge, this paper proposes an enhanced VC approach via run length-based ASCII Huffman (RLAH) encoding, Kernel-based deep Elman neural network (KDENN), together with modified Kalman filters (MKF) algorithms. Initially, the video is transmuted into frames, and the frame's color space model (CSM) is changed as of RGB to YCbCr. Next, the frames are bifurcated into [8 × 8] blocks, and the significant features are extracted as of every block. On account of these features, the KDENN identifies the motion of every block. Those blocks directly undergo a compression process in case of a single motion. Otherwise, MFK smoothens those blocks in order to eradicate the jitters and undesired movements, and then, it goes through compression. After that, RLAH encoding compresses the VF. Then, on the other side, the RLAH decoding algorithm decomposes the video. The results exhibit that the proposed work renders good quality videos with high PSNR value and also it trounces the prevailing compression techniques concerning compression ratio (CR). [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy and slow detection rate of existing target detection algorithms for PCB defect detection, and too many parameters of algorithm model leading to the inability to deploy on mobile terminals, a PCB defect detection algorithm based on MSD-YOLOv5 is proposed. Firstly, to ensure both detection accuracy and speed while reducing the model's size,we combine the lightweight MobileNet-v3 network with the CSPDarknet53 network. Further,the attention mechanism is introduced to highlight the important feature channels and weaken the less useful ones, so as to improve the feature extraction ability of the network. Finally, the coupling detection head is replaced with a decoupling detection head, and the defect location information and category information on the PCB are extracted and learned respectively, which solves the problem of highly coupling of different information feature distributions and enhances the generalization ability of the model. We conduct experiments on a publicly available PCB defect dataset from Peking University using this algorithm. The results show that the proposed method reduces the parameters of YOLOv5 model by 46% and improves the detection accuracy by 3.34%. [ABSTRACT FROM AUTHOR]

Motion estimation is a crucial step in Dynamic Texture (DT) Motion-based recognition systems, as it directly affects the system's overall accuracy and computational proficiency. Whereas efficient motion estimation methods are pixel-based prioritizing recognition quality over computational complexity, this trade-off may not be acceptable in time-sensitive applications. Therefore, Block Matching algorithms (BMA) stand as potential alternative candidates, however, classic BMA often falls into local optimums ambushes resulting suboptimal solutions, particularly for complex motion videos such DT. In this paper, we propose a novel bat-inspired block matching approach for motion estimation to overcome the issue of falling into local optima. Our approach is inspired from the powerful search capacity of the bat algorithm seeking the best block within a search space; aiming accuracy improvement and a faster convergence. Afterwards, we extract motion features from the matched blocks fields to characterize the different DT videos. Two comprehensive sets of experiments were performed to validate the proposed approaches, Firstly the bat-inspired block motion estimation performance was compared against common algorithms on various standard video sequences. Moreover, the effectiveness of the introduced Dynamic Texture Recognition (DTR) system was demonstrated using well-known DT datasets, where comparative studies with state-of-art methods were presented. The results achieved a balance on computational speed and accuracy on multiple datasets. [ABSTRACT FROM AUTHOR]

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module; all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective. [ABSTRACT FROM AUTHOR]

In recent years, graph neural networks (GNNs) have been widely applied in recommender systems. However, existing recommendation algorithms based on GNNs still face challenges in node aggregation and feature extraction processes because they often lack the ability to capture the interactions between users and items, as well as users' multiple intentions. This hinders accurate understanding of users' needs. To address the aforementioned issues, we propose a recommendation model called multi-intent-aware interactive graph convolutional network (Multi-IAIGCN). This model is capable of integrating multiple user intents and adopts an interactive convolution approach to better capture the information on the interaction between users and items. First, before the interaction between users and items begins, user intents are divided and mapped into a graph. Next, interactive convolutions are applied to the user and item trees. Finally, by aggregating different features of user intents, predictions of user preferences are made. Extensive experiments on three publicly available datasets demonstrate that Multi-IAIGCN outperforms existing state-of-the-art methods or can achieve results comparable to those of existing state-of-the-art methods in terms of recall and NDCG, thus verifying the effectiveness of Multi-IAIGCN. [ABSTRACT FROM AUTHOR]

To address the current difficulties in fire detection algorithms, including inadequate feature extraction, excessive computational complexity, limited deployment on devices with limited resources, missed detections, inaccurate detections, and low accuracy, we developed a highly accurate algorithm named YOLOFM. We utilized LabelImg software to manually label a dataset containing 18644 images, named FM-VOC Dataset18644. In addition, we constructed a FocalNext network, which utilized the FocalNextBlock module from the CFnet network. This improves the integration of multi-scale information and reduces model parameters. We also proposed QAHARep-FPN, an FPN network that integrates the structure of quantization awareness and hardware awareness. This design effectively reduces redundant calculations of the model. A brand-new compression decoupled head, named NADH, was also created to enhance the correlation between the decoupling head structure and the calculation logic of the loss function. Instead of using the CIoU loss for bounding box regression, we proposed a Focal-SIoU loss. This promotes the swift convergence of the network and enhances the precision of the regression. The experimental results showed that YOLOFM improved the baseline network's accuracy, recall, F1, mAP50, and mAP50-95 by 3.1%, 3.9%, 3.0%, 2.2%, and 7.9%, respectively. It achieves an equilibrium that combines performance and speed, resulting in a more dependable and accurate solution for detection jobs. [ABSTRACT FROM AUTHOR]