Improved YOLOv5 network-based object detection for anti-intrusion of gantry crane

Niu, Hongchao; Hu, Xiao-Bing; Li, Hang

doi:10.1145/3483845.3483871

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Wang Hong et al [6] used portable ground equipment to collect the spectral reflectance of the pine trees with the diseased beetle and established the spectral characteristics of the diseased pine trees, which achieved an accuracy rate of more than 80% in the prediction of the diseased pine trees. Liu Xialing et al [7] obtained high-resolution images and used a multi-template detection method to identify trees affected by pine forest diseases in different disease stages. The results show that this method can effectively improve the detection efficiency of diseased trees compared with visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is used to optimize the backbone feature extraction network. Furthermore, the YOLOv4 algorithm was improved by improving the backbone network part, adding CBAM attention, and adding the Inceptionv2 structure to reduce the number of model parameters and improve the accuracy and efficiency of identification. The speed and accuracy of the Faster R-CNN, YOLOv4, SSD, YOLOv5, and the improved MobileNetv2-YOLOv4 algorithm were compared, and the detection effects of the Faster R-CNN, YOLOv4, SSD, YOLOv5 and the improved MobileNetv2-YOLOv4 algorithm on trees with pine wilt nematode were analyzed. The experimental results show that the average precision of the improved MobileNetv2-YOLOv4 algorithm is 86.85%, the training time of each iteration cycle is 156 s, the parameter size is 39.23 MB, and the test time of a single image is 15 ms, which is better than Faster R-CNN, YOLOv4, and SSD, but comparable to YOLOv5. Compared with the advantages and disadvantages, comprehensively comparing these four indicators, the improved algorithm has a more balanced performance in the detection speed, the parameter size, and the average precision. The F1 score of the improved algorithm (95.60%) was higher than that of Faster R-CNN (90.80%), YOLOv4 (94.56%), and SSD (92.14%), which met the monitoring requirements of pine wilt nematode trees. Faster R-CNN and SSD pine-wilt-nematode tree detection models are not ideal in practical applications. Compared with the YOLOv4 pine-wilt-nematode tree detection model, the improved MobileNetv2-YOLOv4 algorithm satisfies the condition of maintaining a lower model parameter quantity to obtain higher detection accuracy; therefore, it is more suitable for practical application scenarios of embedded devices. It can be used for the rapid detection of pine wilt nematode diseased trees.

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Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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“…PANet is a kind of feature pyramid network, which consists of a pyramid pooling module, convolution layer, and upper sampling layer [9] . As shown in Figure 6, in this paper, PANet is inserted between the Down Sampling Module and the YOLOv5 Head in the YOLOv5 structure [10] . First, the feature maps of different scales in the feature pyramid are aggregated, and then further processed through the convolution layer, and the feature maps are expanded by upsampling.…”

Section: Improvement To Yolov5 Networkmentioning

confidence: 99%

Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Xu,

Guo

2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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In the field of classification and recognition of mechanical parts, in order to solve the difficulties of small target detection, adhesion and overlapping parts classification and recognition, this paper proposes an automatic classification and recognition algorithm of mechanical parts based on improved convolutional neural network YOLOv5. The feature pyramid module PANet is added to the traditional YOLOv5 network structure to strengthen the characterization ability of the network. At the same time, NMS algorithm and image segmentation algorithm are used to improve the detection ability of network boundary segmentation, to improve the detection accuracy and recall rate. The experimental results show that both the traditional YOLOv5 and the improved YOLOv5 in this paper can realize the classification and identification of mechanical parts, and the improved YOLOv5 has better detection precision and accuracy, faster detection speed, and better network robustness.

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“…The backbone network includes Focus operation, Convolutional layer, ResNet Block [32], and spatial pyramid pooling (SPP) net. The Focus structure [19] can adjust the number of channels without much information loss. Four ResNet [20] Modules form the body of the Yolov5 backbone, each of them including some CNN-ResNet Blocks (Figure 4b).…”

Section: Yolov5 Backbonementioning

confidence: 99%

“…The Focus structure [19] can adjust the number of channels without much information loss. Four ResNet [20] Modules form the body of the Yolov5 backbone, each of them including some CNN‐ResNet Blocks (Figure 4b).…”

Section: Approach‐related Workmentioning

confidence: 99%

Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5

Ding,

Chen,

Chen

et al. 2023

IET Renewable Power Gen

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At present, the domestic photovoltaic (PV) industry is developing rapidly. In order to improve the production efficiency of PV cells, a fast and accurate automatic detection model of PV modules’ defects that can be applied in the production line is essential. In this paper, based on the characteristics of significant differences in PV module defect size and a large number of fine defects, an improved defect detection algorithm based on Yolov5 is proposed. Thirteen mainstream defects are divided into two categories according to size, and a series‐connected detection network is constructed for a two‐stage detection. In order to better detect fine defects, this paper proposes the TR‐ResNet module, a residual module composed of the self‐attention, based on the self‐attention mechanism, to replace some fully convolutional residual modules (CNN‐ResNet module) in the Yolov5 backbone network. After testing, the Precision, Recall and mAP of the model are greatly improved and gained 0.904, 0.845, and 0.840. Moreover, the model performs well in stability detection, which can adapt to different production environments and quality requirements. The present study may make the detection work more efficient and improve productivity.

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Improved YOLOv5 network-based object detection for anti-intrusion of gantry crane

Cited by 12 publications

References 3 publications

Detection of Pine Wilt Nematode from Drone Images Using UAV

Detection of Pine Wilt Nematode from Drone Images Using UAV

Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5

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