A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

Liu, Cong; Yi, Wentao; Liu, Min; Wang, Yifeng; Hu, Sheng; Wu, Minghu

doi:10.3390/electronics12204292

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…The YOLO-Pose network structure, as illustrated in Figure 3, is built upon the foundation of the YOLOv5 network structure. The YOLOv5 network structure comprises three main components: Backbone, Neck, and Head [16], with the detailed flowchart depicted in Figure 3. The significance of YOLOv5 lies in its concise, user-friendly, and efficient characteristics, which have swiftly established its position in the industrial sector and enabled its widespread application in practical production fields.…”

Section: Yolo-pose Human Posture Estimation Algorithmmentioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

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Section: Yolo-pose Human Posture Estimation Algorithmmentioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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An insulator target detection algorithm based on improved YOLOv5

Zeng,

Zhou,

Zhou

et al. 2025

Sci Rep

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Research on the application of lightweight deep learning model in automatic identification of electricity-related public safety hazards in distribution lines

Feng,

Wei

2024

International Journal of Low-Carbon Technologies

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In view of the current problems of low speed, high network complexity, and difficulty in accurately detecting small target defects in insulator defect detection methods, this study proposes a lightweight insulator defect detection model. First, by enhancing both the convolutional block (CBL) and the efficient long-range aggregation network (ELAN-S) within the feature extraction network, the extraction capability for defect features is significantly augmented. Secondly, the CA-Tiny Spatial Pyramid Pooling (SPP) module was crafted by seamlessly integrating the coordinate attention mechanism with Tiny SPP, enabling the model to prioritize insulator defect characteristics and thereby enhancing defect detection accuracy. Finally, utilizing the positioning loss function WIoUv3 loss for loss calculation, a smaller gradient gain is assigned to low-quality anchor boxes, minimizing harmful gradients and enhancing the model’s positioning performance. Experimental outcomes demonstrate that the enhanced YOLOv7-tiny model excels in rapid and precise defect detection. In comparison to the original YOLOv7-tiny model, the proposed version is well-suited for edge device deployment, enabling real-time insulator defect detection.

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A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection

Cited by 4 publications

References 31 publications

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

An insulator target detection algorithm based on improved YOLOv5

Research on the application of lightweight deep learning model in automatic identification of electricity-related public safety hazards in distribution lines

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