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Timely detection of plant diseases is crucial for agricultural safety, product quality, and environmental protection. However, plant disease detection faces several challenges, including the diversity of plant disease scenarios and complex backgrounds. To address these issues, we propose a plant disease detection model named PYOLO. Firstly, the model enhances feature fusion capabilities by optimizing the PAN structure, introducing a weighted bidirectional feature pyramid network (BiFPN), and repeatedly fusing top and bottom scale features. Additionally, the model's ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. Finally, the MHC2f mechanism is designed to enhance the model's ability to perceive complex backgrounds and targets at different scales by utilizing its self-attention mechanism for parallel processing. Experiments demonstrate that the model's mAP value increases by 4.1% compared to YOLOv8n, confirming its superiority in plant disease detection. [ABSTRACT FROM AUTHOR]

With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network's ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model's detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%. [ABSTRACT FROM AUTHOR]

Currently, mural object detection is highly dependent on traditional manual detection means, which is inefficient and prone to frescoe damage. Therefore, We propose an enhanced mural image detection algorithm, Brg-YOLO, based on YOLOv8, to achieve efficient, non-contact automatic detection. First, We enhance detection across scales and complex scenes by incorporating a bidirectional feature pyramid network (BiFPN) in the neck, enabling efficient multi-scale feature reuse and improved feature fusion. In addition, we embed the residual squeezing-and-excitation (RSE) attention module in the backbone to mitigate the feature aliasing effect. Finally, with the Ghost+RSE Bottleneck design in the Neck part, we realize a lightweight model deployment that maintains the excellent detection effect while reducing the number of parameters. The experimental results show that the model achieves 84.6% and 47.8% for mAP@0.5 and mAP@0.5:0.95, respectively, in the mural object detection task, which far exceeds similar methods. This study provides new perspectives and tools for mural painting conservation and research, realizes efficient and accurate mural detection through non-contact automatic detection methods, and creates a new paradigm for mural heritage conservation. [ABSTRACT FROM AUTHOR]

Real-time detection of rockfall on slopes is an essential part of a smart worksite. As a result, target detection techniques for rockfall detection have been rapidly developed. However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challenges to this research. In this paper, we propose an enhanced high-speed slope rockfall detection method based on YOLOv8n. First, the LSKAttention mechanism is added to the backbone part to improve the model's ability to balance the processing of global and local information, which enhances the model's accuracy and generalization ability. Second, in order to ensuredetection accuracy for smaller targets, an enhanced detection head is added, and other detection heads of different sizes are combined to form a multi-scale feature fusion to improve the overall detection performance. Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. In addition we compare the LSKAttention mechanism with other attention mechanisms to verify the effectiveness of the improvements. Compared with the baseline model, our method improves the average accuracy mAP@0.5 by 4.8%. Moreover, the amount of parameters is reduced by 20.2%. Among the different evaluation criteria, the LHB-YOLOv8 method shows obvious advantages, making it suitable for engineering applications and the practical deployment of slope rockfall detection systems. [ABSTRACT FROM AUTHOR]

A lightweight YOLOv5 improved algorithm-based inspection model is proposed to address the problems of defective printed circuit boards (PCBs), which are difficult to identify. First, the detection part of YOLOv5 is changed to dual-head detection to significantly improve the inference speed of the model on edge devices and adapt to the real-time target detection requirements. Second, the introduction of GSConv in the Neck part helps to further reduce the number of parameters of the model and improve the computational efficiency, which can enhance the model’s capture ability. Finally, BiFPN is introduced to fuse multi-scale information to enhance the model’s detection ability for targets of different sizes. The experimental results show that the improved lightweight YOLOv5 algorithm in this paper achieves 94.9% in the average accuracy mean (mAP@0.5), which is only 0.5 percentage points less compared to the original YOLOv5 algorithm. However, the improved algorithm has 56.2% fewer floating point operations (GFLOPs) and 53.7% fewer parameters. This improvement not only makes the algorithm more accurate and lightweight, but also significantly improves the efficiency of PCB inspection, which better meets the needs of industrial production. [ABSTRACT FROM AUTHOR]

Although existing object detection algorithms have achieved excellent detection accuracy, with the continuous improvement of detection accuracy, the parameters of the model are getting larger and larger, and the model complexity is getting higher and higher, which makes it difficult to deploy the object detection algorithms on the edge end and mobile end. In order to improve the application of the object detection algorithm on edge and mobile, this paper proposes a lightweight object detection algorithm, CAL-SSD, using a coordinated attention mechanism. First, we embed the coordinated attention mechanism into MobileNetv2 to form CA_MobileNetv2 as the backbone of the CAL-SSD object detection algorithm, significantly reducing the model parameters and complexity and improving the network’s ability to differentiate between object and background. Second, we design a super-resolution feature fusion module (SFFM) to introduce deep semantic information into shallow feature maps. Then, we use depthwise separable convolution instead of traditional 3×3 convolution to construct additional feature layers and detection heads, further reducing the model parameters. Finally, we employ BiFPN to construct a new feature pyramid to utilize the multi-scale features of the target fully. Experimental results on the PASCAL VOC and MS COCO datasets show that CAL-SSD significantly reduces the model parameters and complexity and achieves an optimal balance of speed and accuracy. [ABSTRACT FROM AUTHOR]

The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance. Firstly, we integrate a Transformer module based on the self-attention mechanism with the C3 module at the end of the backbone network to form a C3TR module. This integration enhances the global feature extraction capability of the backbone network and improves its ability to detect small target wear particles. Secondly, we introduce the convolutional block attention module (CBAM) into the neck network to enhance salience for detecting wear particles while suppressing irrelevant information interference. Furthermore, multi-scale feature maps extracted by the backbone network are fed into the bidirectional feature pyramid network (BiFPN) for feature fusion to enhance the model's ability to detect wear particle feature maps at different scales. Lastly, Ghost modules are introduced into both the backbone network and the neck network to reduce their complexity and improve detection speed. Experimental results demonstrate that TCBGY-Net achieves outstanding precision in detecting wear particles against complex backgrounds, with a mAP@0.5 value of 98.3%, which is a 10.2% improvement over YOLOv5s. In addition, we conducted comprehensive ablation experiments, to validate the contribution of each module and the robustness of our model. TCBGY-Net also outperforms most current mainstream algorithms in terms of detection speed, with up to 89.2 FPS capability, thus providing favorable conditions for subsequent real-time online monitoring of changes in wear particles and fault diagnosis in ship power systems. [ABSTRACT FROM AUTHOR]

The current bolt detection for overhead work robots used for transmission lines faces the problems of lightweight algorithms and high accuracy of target detection. To address these challenges, this paper proposes a lightweight bolt detection algorithm based on improved YOLOv8 (you only look once v8) model. Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. Secondly, the P2 small object detection layer is introduced into the neck structure and the BiFPN network structure is incorporated to enhance the bidirectional connection paths, thereby promoting the upward and downward propagation of features. It improves the accuracy of the network for bolt‐small target detection. The experimental results show that, compared to the original YOLOv8 model, the proposed algorithm demonstrates superior performance on a self‐collected dataset. The mAP accuracy is improved in this paper by 9.9%, while the number of model parameters and the model size is reduced by 0.973 × 106 and 1.7 MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast detection and recognition of obstacles such as people, tractors, and electric power pylons in the field. This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model's capacity to distinguish between the background and obstacles. Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector's overall performance. A dataset comprising 5963 images of people, electric power pylons, telegraph poles, tractors, and harvesters in a farmland environment was constructed. The training set comprised 4771 images, while the validation and test sets each consisted of 596 images. The results from the experiments indicated that the enhanced model attained precision, recall, and average precision scores of 85.5%, 75.1%, and 82.5%, respectively, on the custom dataset. This reflected increases of 1.3, 1.2, and 1.9 percentage points when compared to the baseline YOLOv8 model. Furthermore, the model reached 52 detection frames per second, thereby significantly enhancing the detection performance for common obstacles in the field. The model enhanced by the previously mentioned techniques guarantees a high level of detection accuracy while meeting the criteria for real-time obstacle identification in unmanned agricultural equipment during fieldwork. [ABSTRACT FROM AUTHOR]

Fluid inclusions have important guiding significance for oil and gas resource evaluation, reservoir geochemistry, fluid types, fluid sources, and exploration. However, The identification of fluid inclusions primarily relies on manual searching, a process that is time-consuming and labor-intensive. To address this issue, an improved YOLOv5s fluid inclusion object detection algorithm was proposed. The feature extraction and feature fusion components of the original YOLOv5s model were enhanced to improve the model's detection capability, making it more suitable for fluid inclusion detection. A coordinate attention mechanism was introduced in the feature extraction component to enhance localization and recognition capabilities. Additionally, the original path aggregation network in the feature fusion component was replaced with a bidirectional feature pyramid network. The upgraded network possesses stronger feature fusion capabilities, thereby enhancing the detection capability of small targets. Experimental results demonstrate that compared to the original YOLOv5s model, the average precision of the improved YOLOv5s increases from 75. 3% to 77. 3%, representing a 2% improvement over the original algorithm. The detection speed also improves from 58. 14 frames/ s to 62. 89 frames/ s, resulting in a 4. 75 frames/ s improvement, thus achieving more accurate and efficient fluid inclusion detection. [ABSTRACT FROM AUTHOR]

Wheat head identification and localization are crucial for field management and yield estimation. Traditional wheat head detection relies on labor-intensive manual counting. To address this issue, this study establishes a densely populated wheat head dataset and correspondingly proposes an efficient, rapid real-time model of CB-YOLO, which incorporates a Convolutional Block Attention Module (CBAM) mechanism (a sophisticated fusion of spatial attention mechanism and channel attention mechanism) so as to integrate channel and spatial features. Also, with an incorporated Bidirectional Feature Pyramid Network (BiFPN) to the neck, crowded wheat head detection was enhanced by establishing connections from the backbone to the bottom-up path. This enhancement surpasses the performance of the basic YOLOv5L model, achieving AP50 of 94.3% and AP50:95 of 53.9%. Among 10 classical detection models, CB-YOLO has a relatively small parameter size of 44Mb and high computing speed of 108G FLOPs. Additionally, ablation experiments demonstrate that compared to YOLOv5L without CBAM and BiFPN, the feature fusion module improves AP50 from 93.2% to 94.3%, indicating its effectiveness in enhancing detection performance. In conclusion, CB-YOLO exhibits outstanding identification performance, showing its feasibility in practical agricultural applications, and future work will focus on improving its localization ability. [ABSTRACT FROM AUTHOR]

Interest in anti-UAV detection systems has increased due to growing concerns about the security and privacy issues associated with unmanned aerial vehicles (UAVs). Achieving real-time detection with high accuracy, while accommodating the limited resources of edge-computing devices poses a significant challenge for anti-UAV detection. Existing deep learning-based models for anti-UAV detection often cannot balance accuracy, processing speed, model size, and computational efficiency. To address these limitations, a lightweight and efficient anti-UAV detection model, DRBD-YOLOv8, is proposed in this paper. The model integrates several innovations, including the application of a Re-parameterization Cross-Stage Efficient Layered Attention Network (RCELAN) and a Bidirectional Feature Pyramid Network (BiFPN), to enhance feature processing capabilities while maintaining a lightweight design. Furthermore, DN-ShapeIoU, a novel loss function, has been established to enhance detection accuracy, and depthwise separable convolutions have been included to decrease computational complexity. The experimental results showed that the proposed model outperformed YOLOV8n in terms of mAP50, mAP95, precision, and FPS while reducing GFLOPs and parameter count. The DRBD-YOLOv8 model is almost half the size of the YOLOv8n model, measuring 3.25 M. Its small size, fast speed, and high accuracy combine to provide a lightweight, accurate device that is excellent for real-time anti-UAV detection on edge-computing devices. [ABSTRACT FROM AUTHOR]

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n's detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect. [ABSTRACT FROM AUTHOR]

With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management. [ABSTRACT FROM AUTHOR]

Insulator defect detection is pivotal for the reliable functioning of power transmission and distribution networks. This paper introduces an optimized lightweight model for insulator defect detection, RDB-YOLOv8n, which addresses the limitations of existing models including high parameter counts, extensive computational demands, slow detection speeds, low accuracy, and challenges in deployment to embedded terminals. First, the RDB-YOLOv8n model employs a novel lightweight module, C2f_RBE, in its Backbone architecture. This module replaces conventional Bottlenecks with RepViTBlocks and SE modules with EMA attention mechanisms, significantly enhancing detection efficiency and performance. Secondly, the Neck of the model incorporates the C2f_DWFB module, which substitutes Bottlenecks with FasterBlocks and introduces depth-wise separable convolutions (DWConv) over standard convolutions to ensure accuracy and robustness in complex environments. Additionally, the integration of a BiFPN structure within the Neck network further reduces the parameters and computational load of the model. while simultaneously improving feature fusion capabilities and detection efficiency. Experimental results show that the enhanced RDB-YOLOv8n model achieves a 41.2% reduction in parameters and a decrease in GFLOPs from 8.1 to 7.1, with a model size reduction of 39.1% and an increase in mAP(0.5) by 1.7%, meeting the requirement of real-time and efficient accurate detection of insulator defects. [ABSTRACT FROM AUTHOR]

With the development of apple-picking robots, deep learning models have become essential in apple detection. However, current detection models are often disrupted by complex backgrounds, leading to low recognition accuracy and slow speeds in natural environments. To address these issues, this study proposes an improved model, YOLOv8s-CFB, based on YOLOv8s. This model introduces partial convolution (PConv) in the backbone network, enhances the C2f module, and forms a new architecture, CSPPC, to reduce computational complexity and improve speed. Additionally, FocalModulation technology replaces the original SPPF module to enhance the model’s ability to recognize key areas. Finally, the bidirectional feature pyramid (BiFPN) is introduced to adaptively learn the importance of weights at each scale, effectively retaining multi-scale information through a bidirectional context information transmission mechanism, and improving the model’s detection ability for occluded targets. Test results show that the improved YOLOv8 network achieves better detection performance, with an average accuracy of 93.86%, a parameter volume of 8.83 M, and a detection time of 0.7 ms. The improved algorithm achieves high detection accuracy with a small weight file, making it suitable for deployment on mobile devices. Therefore, the improved model can efficiently and accurately detect apples in complex orchard environments in real time. [ABSTRACT FROM AUTHOR]

The small area of a printed circuit board (PCB) results in densely distributed defects, leading to a lower detection accuracy, which subsequently impacts the safety and stability of the circuit board. This paper proposes a new YOLO-BFRV network model based on the improved YOLOv8 framework to identify PCB defects more efficiently and accurately. First, a bidirectional feature pyramid network (BIFPN) is introduced to expand the receptive field of each feature level and enrich the semantic information to improve the feature extraction capability. Second, the YOLOv8 backbone network is refined into a lightweight FasterNet network, reducing the computational load while improving the detection accuracy of minor defects. Subsequently, the high-speed re-parameterized detection head (RepHead) reduces inference complexity and boosts the detection speed without compromising accuracy. Finally, the VarifocalLoss is employed to enhance the detection accuracy for densely distributed PCB defects. The experimental results demonstrate that the improved model increases the mAP by 4.12% compared to the benchmark YOLOv8s model, boosts the detection speed by 45.89%, and reduces the GFLOPs by 82.53%, further confirming the superiority of the algorithm presented in this paper. [ABSTRACT FROM AUTHOR]

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model's feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Technology Natural Science Edition is the property of Henan University of Technology Journal Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network’s ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model’s detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%.

In order to improve the efficiency and precision of underground drill pipe counting in coal mines, a coal mine underground drill pipe counting method based on the improved YOLOv8n model is proposed. The YOLOv8n-TbiD is established.The model can accurately detects and segments drill pipes in mine drilling rig working videos. The main improvements include the following points. In order to effectively capture the boundary information of drill rods and improve the precision of the model in recognizing drill rod shapes, the weighted bidirectional feature pyramid network (BiFPN) is used instead of the path aggregation network (PANet). To address the issue of drill pipe objects being easily confused with dim mine environments, Triplet Attention is added to the SPPF module of the Backbone network to enhance the model's capability to suppress background interference. In response to the small proportion of drill pipes in the image and the complexity of background information, the Dice loss function is used to replace CIoU loss function to optimize the segmentation processing of drill pipe objects in the model. The method uses the YOLOv8n-TBiD model to segment the drill pipe and its mask information. A drill pipe counting algorithm is designed based on the rule that the mask area of the drill pipe decreases during drilling and suddenly increases when a new drill pipe is installed. The working video of the drilling rig in the fully mechanized working face is selected, in order to conduct experimental verification of drill pipes counting method based on YOLOv8n-TBiD model. The experimental results show that the mean average precision of the YOLOv8n-TBiD model for detecting drill pipes reaches 94.9%. Compared with the comparative experimental models GCI-YOLOv4, ECO-HC, P-MobileNetV2, YOLOv5, and YOLOX, the accuracy increases by 4.3%, 7.5%, 2.1%, 6.3%, and 5.8%, respectively, and the detection speed increases by 17.8% compared to the original YOLOv8n model. The proposed drill pipe counting algorithm achieves precision of 99.3% on video datasets from different underground coal mine environments.

Abstract Detecting foreign objects in power transmission lines is essential for mitigating safety risks and maintaining line stability. Practical detection, however, presents challenges including varied target sizes, intricate backgrounds, and large model weights. To address these issues, this study introduces an innovative GEB-YOLO model, which balances detection performance and quantification. Firstly, the algorithm features a lightweight architecture, achieved by merging the GhostConv network with the advanced YOLOv8 model. This integration considerably lowers computational demands and parameters through streamlined linear operations. Secondly, this paper proposes a novel EC2f mechanism, a groundbreaking feature that bolsters the model’s information extraction capabilities. It enhances the relationship between weights and channels via one-dimensional convolution. Lastly, the BiFPN mechanism is employed to improve the model’s processing efficiency for targets of different sizes, utilizing bidirectional connections and swift feature fusion for normalization. Experimental results indicate the model’s superiority over existing models in precision and mAP, showing improvements of 3.7 and 6.8%, respectively. Crucially, the model’s parameters and FLOPs have been reduced by 10.0 and 7.4%, leading to a model that is both lighter and more efficient. These advancements offer invaluable insights for applying laser technology in detecting foreign objects, contributing significantly to both theory and practice.

In order to improve the poor accuracy of steel surface defects caused by poor detection of small targets, on the basis of the YOLOv5s, by adding the SE (attention mechanism) in the backbone network mechanism, C2f module instead of C3 module, the BIFPN (bidirectional feature pyramid network) instead of the PAN (path aggregation network) network in the neck,these three methods were used to investigate the improvement of the ability to the defect of small target detection. It aims to improve the detection accuracy and achieve real-time detection. The results show that the mAP (mean average precision) of the improved YOLOv5s-SCB algorithm on NEU-DET (northeastern university-detect) reaches 77. 9%, which is 3. 7% higher than that of the YOLOv5s network on the premise of real-time detection. Compared with other improved algorithms based on YOLOv5s and YOLOv8, YOLOv5S-SCB achieves better detection effect. It is concluded that the proposed steel surface defect detection algorithm YOLOv5S-SCB can better detect defects on steel surfaces. [ABSTRACT FROM AUTHOR]

This study proposes an embedded traffic sign detection system, YOLOV5-MCBS, based on an enhanced YOLOv5 algorithm. This system aims to mitigate the impact of traditional target detection algorithms' high computational complexity and low detection accuracy on traffic sign detection performance, thereby improving accuracy and real-time performance. Our primary objective is to develop a lightweight network that enhances detection accuracy, enabling real-time detection on embedded systems. First, to minimize computation and model size, we replaced the original YOLOv5 algorithm's backbone feature network with a lightweight MobileNetV3 network. Subsequently, we introduced the convolutional block attention module into the neck network to optimize the feature fusion stage's attention and enhance model detection accuracy. Concurrently, we employed the bidirectional feature pyramid network in the neck layer for multi-scale feature fusion. Additionally, we incorporated a small target detection layer into the original network output layer to enhance detection performance. What's more, we transplanted the enhanced algorithm into a Raspberry Pi embedded system to validate its real-time detection performance. Finally, we conducted computer simulations to assess our algorithm's performance by comparing it with existing target detection algorithms. Experimental results suggest that the enhanced algorithm achieves an average precision mean (mAP @ 0.5) value of 95.3% and frames per second value of 91.1 on the embedded system. [ABSTRACT FROM AUTHOR]

Currently, the use of deep learning technologies for detecting defects in transmission line insulators based on images obtained through unmanned aerial vehicle inspection simultaneously presents the problems of insufficient detection accuracy and speed. Therefore, this study first introduced the bidirectional feature pyramid network (BiFPN) module into YOLOv5 to achieve high detection speed as well as enable the combination of image features at different scales, enhance information representation, and allow accurate detection of insulator defect at different scales. Subsequently, the BiFPN module and simple parameter-free attention module (SimAM) were combined to improve the feature representation ability and object detection accuracy. The SimAM also enabled fusion of features at multiple scales, further improving the insulator defect detection performance. Finally, multiple experimental controls were designed to verify the effectiveness and efficiency of the proposed model. The experimental results obtained using self-made datasets show that the combined BiFPN and SimAM model (i.e., the improved BaS-YOLOv5 model) performs better than the original YOLOv5 model; the precision, recall, average precision and F1 score increased by 6.2%, 5%, 5.9%, and 6%, respectively. Therefore, BaS-YOLOv5 substantially improves detection accuracy while maintaining a high detection speed, meeting the requirements for real-time insulator defect detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development. [ABSTRACT FROM AUTHOR]

Introduction: The secure operation of electric power transmission lines is essential for the economy and society. However, external factors such as plastic film and kites can cause damage to the lines, potentially leading to power outages. Traditional detection methods are inefficient, and the accuracy of automated systems is limited in complex background environments. Methods: This paper introduces a Weighted Spatial Attention (WSA) network model to address the low accuracy in identifying extraneous materials within electrical transmission infrastructure due to background texture occlusion. Initially, in the model preprocessing stage, color space conversion, image enhancement, and improved Large Selective Kernel Network (LSKNet) technology are utilized to enhance the model's proficiency in detecting foreign objects in intricate surroundings. Subsequently, in the feature extraction stage, the model adopts the dynamic sparse BiLevel Spatial Attention Module (BSAM) structure proposed in this paper to accurately capture and identify the characteristic information of foreign objects in power lines. In the feature pyramid stage, by replacing the feature pyramid network structure and allocating reasonable weights to the Bidirectional Feature Pyramid Network (BiFPN), the feature fusion results are optimized, ensuring that the semantic information of foreign objects in the power line output by the network is effectively identified and processed. Results: The experimental outcomes reveal that the test recognition accuracy of the proposed WSA model on the PL (power line) dataset has improved by three percentage points compared to that of the YOLOv8 model, reaching 97.6%. This enhancement demonstrates the WSA model's superior capability in detecting foreign objects on power lines, even in complex environmental backgrounds. Discussion: The integration of advanced image preprocessing techniques, the dynamic sparse BSAM structure, and the BiFPN has proven effective in improving detection accuracy and has the potential to transform the approach to monitoring and maintaining power transmission infrastructure. [ABSTRACT FROM AUTHOR]

A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that are caused by the large-scale variation of surface defects, inconspicuous small defect characteristics, and a lack of concentrated feature information in defect areas. Firstly, an improved CBAM (Channel-Wise Attention Module) convolutional attention module is employed, which effectively focuses on the feature information of defect areas in the aluminum defect dataset with only a small amount of spatial dimension. Secondly, a bidirectional weighted feature fusion network is utilized, incorporating a multi-scale feature fusion network with skip connections to aggregate various high-resolution features, thus enriching the semantic expression of features. Then, new size feature maps that have not been fused are introduced into the detection layer network to improve the detection effect of small target defects. Experimental results indicate that an average detection accuracy (mAP) of 82.6% was achieved by the improved YOLOv5 algorithm on the aluminum surface defect dataset. An improvement of 6.2% over the previous version was observed. The current defect detection requirements of aluminum profile production sites are met by this enhanced algorithm. [ABSTRACT FROM AUTHOR]

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields. [ABSTRACT FROM AUTHOR]

X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people's lives and property. Due to the large number of parameters and high computational complexity, most current object detection models are challenging to deploy on portable mobile security inspection devices. Therefore, this paper proposes an improved YOLOv7-tiny model for application in prohibited item detection tasks. Firstly, the feature extraction backbone network is replaced with the lightweight GhostNet network to reduce computational complexity and improve detection speed. Secondly, the FPN in the Neck is replaced with BiFPN, further reducing computational complexity and memory access through skip connections. Finally, a lightweight CA attention mechanism is embedded between the Backbone and Neck layers, and the Focal-EIoU Loss function is employed to enhance the detection capability for small-sized items. Experimental results on the SIXray public dataset show a 14.8% reduction in model parameters, a 19.7% reduction in computational complexity, and a 15.9% reduction in volume after the improvements. The detection speed increases from 82.4 to 90.2, and the detection accuracy for prohibited items reaches 90.3%. The experimental results demonstrate that the improved model achieves overall lightweighting while maintaining a high detection rate and improving detection speed. [ABSTRACT FROM AUTHOR]

In recent years, achieving high-precision and high-speed target detection of surface defects on aluminum profiles to meet the requirements of industrial applications has been challenging. In this paper, the GEB-YOLO is proposed based on the YOLOv7 algorithm. First, the global attention mechanism (GAM) is introduced, highlighting defect features. Second, the Explicit Visual Center Block (EVCBlock) is integrated into the network for key information extraction. Meanwhile, the BiFPN network structure is adopted to enhance feature fusion. The ablation experiments have demonstrated that the defect detection accuracy of the GEB-YOLO model is improved by 6.3%, and the speed is increased by 15% compared to the YOLOv7 model. [ABSTRACT FROM AUTHOR]

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Technology Natural Science Edition is the property of Henan University of Technology Journal Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distracted driving is one of the leading causes of traffic accidents and has become a bottleneck for improving driver assistance technologies. It is still a challenge to detect distracted driving behavior in real-life scenarios, which have the features of complex backgrounds, different target scales, and resolutions. In this context, a lightweight YOLOv5-GBC model is proposed for real-time distracted driving detection in this work. Firstly, the lightweight network GhostConv is used to perform lightweight operations on the convolutional layers, aiming to reduce a large number of parameters and computations. Secondly, the path aggregation network structure is improved to enhance the model fusion ability for different scale features, and coordinated attention is introduced to enhance the model extraction ability for effective information. The proposed YOLOv5-GBC model can predict different types of distracted driving. Finally, this work conducts extensive experiments; the results show that the proposed model has a mean accuracy (mAP) of 91.8%, which is 3.9% better than the baseline model, with a reduction of 6.5% and 9.1% in the weight file and Floating-point Operations Per Second, respectively. It outperforms the models of Faster-RCNN, SSD, YOLOv3-tiny, and YOLOv4-tiny, which indicates that the proposed model can identify distracted driving behaviors efficiently and rapidly. [ABSTRACT FROM AUTHOR]

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications. [ABSTRACT FROM AUTHOR]

Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese character stroke extraction and recognition, as well as unclear segmentation. This study builds upon the YOLOv8n-seg model to propose the YOLOv8n-seg-CAA-BiFPN Chinese character stroke fine segmentation model. The proposed Coordinate-Aware Attention mechanism (CAA) divides the backbone network input feature map into four parts, applying different weights for horizontal, vertical, and channel attention to compute and fuse key information, thus capturing the contextual regularity of closely arranged stroke positions. The network's neck integrates an enhanced weighted bi-directional feature pyramid network (BiFPN), enhancing the fusion effect for features of strokes of various sizes. The Shape-IoU loss function is adopted in place of the traditional CIoU loss function, focusing on the shape and scale of stroke bounding boxes to optimize the bounding box regression process. Finally, the Grad-CAM++ technique is used to generate heatmaps of segmentation predictions, facilitating the visualization of effective features and a deeper understanding of the model's focus areas. Trained and tested on the public Chinese character stroke datasets CCSE-Kai and CCSE-HW, the model achieves an average accuracy of 84.71%, an average recall rate of 83.65%, and a mean average precision of 80.11%. Compared to the original YOLOv8n-seg and existing mainstream segmentation models like SegFormer, BiSeNetV2, and Mask R-CNN, the average accuracy improved by 3.50%, 4.35%, 10.56%, and 22.05%, respectively; the average recall rates improved by 4.42%, 9.32%, 15.64%, and 24.92%, respectively; and the mean average precision improved by 3.11%, 4.15%, 8.02%, and 19.33%, respectively. The results demonstrate that the YOLOv8n-seg-CAA-BiFPN network can accurately achieve Chinese character stroke segmentation. [ABSTRACT FROM AUTHOR]

The detailed, high-resolution images captured by drones pose challenges to target detection algorithms with complex scenes and small-sized targets. Moreover, targets in unmanned aerial vehicle images are usually affected by factors such as viewing perspective, occlusion, and light, which increase the difficulty of target detection. In response to the above issues, we propose an improved SDMSEAFYOLOv8 for target detection based on YOLOv8, combined with a Bidirectional Feature Pyramid Network, to improve the sensing ability of the model for multiscale targets. A Space-to-depth layer replaces the traditional strided convolution layer to enhance the extraction of fine-grained information and small-sized target features. A Multi-Separated and Enhancement Attention module enhances the feature learning ability of the occluded target region, thus reducing missed and false detections. Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. The conventional non-maximum suppression algorithm is improved, so as to reduce the problem of missed detections under a densely occluded scene by setting the attenuation function to adjust the confidence of the treated box based on the overlap between it and the highest-scoring box. Experiments demonstrate that the accuracy of SDMSEAF-YOLOv8 exceeds that of state-of-the-art models on the VisDrone2019-DET-val dataset, with a mAP of 42.9% at 640-pixel resolution, 14.8% over the baseline YOLOv8-x algorithm model, and 6.0% over the known state-of-the-art Fine-Grained Target Focusing Network model and with twice as fast detection. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel's production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model's adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model's mAP50 still exceeds 70%, demonstrating the model's strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency. [ABSTRACT FROM AUTHOR]

In order to solve the problem of small targets being prone to false detection and missed detection in aircraft skin defect detection under complex backgrounds, the model of aircraft skin defect detection based on improved YOLOv8n is proposed in this paper. Firstly, the Shuffle Attention + + module is incorporated into the network, combined with the residual connection idea, to more efficiently fuse feature map information; Secondly, SIOU and Focal Loss are used to replace CIOU as the regression loss functions to balance positive and negative samples in complex backgrounds and accelerate model convergence; Subsequently, the bidirectional feature pyramid network is used to modify the detection head and enhance multi-scale feature fusion. Furthermore, the depth-wise convolution module is used to replace the convolution module (Conv) in the neck part, which serves to reduce the parameters of the model and speed up the detection speed. Finally, an aircraft skin defect dataset is established, combined with Mosaic data enhancement to prevent the model from overfitting, and adopted the class balancing strategy to avoid class bias. The experimental results show that the detection accuracy of our improved YOLOv8n model is 97.9%, which is 7.3% higher than the baseline model. The model's recall rate, the mean average precision, and F1 scores are improved by 13.9%, 6.6%, and 11.0%, respectively. The detection speed has achieved 139FPS, fulfilling the requirements of high accuracy and real-time performance in small target aircraft skin defect detection tasks. [ABSTRACT FROM AUTHOR]