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<p>Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an improved YOLOv5 model. Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. Secondly, to broaden the image receptive field and improve feature fusion, a Receptive Field Block (RFB) model is incorporated into the neck network, replacing the original Spatial Pyramid Pooling Fast (SPPF) module. Additionally, a Lattice Structured Kernel (LSKBlock) attention mechanism is appended at the end of the neck network to further obtain richer semantic information. Finally, to flexibly improve the accuracy of bounding boxes of different sizes and enhance the robustness of the model, an <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2024_84623_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">\(\alpha CIOU\)</EquationSource> </InlineEquation> loss function is utilized to replace the original Complete Intersection Over Union (CIOU) loss function. Experimental results demonstrate that the improved YOLOv5 model achieves a mean Average Precision (mAP) precision of 95.60%, with a parameter count of 18.36&#xa0;M and a computational load of 30.10G, respectively. The Precision (P) and Recall (R) are 88.10% and 95.20%, providing strong support for deployment on mobile devices for real-time detection.</p>

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An insulator target detection algorithm based on improved YOLOv5

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • Bing Zeng,
  • Zhihao Zhou,
  • Yu Zhou,
  • Dilin He,
  • Zhanpeng Liao,
  • Zihan Jin,
  • Yulu Zhou,
  • Kexin Yi,
  • Yunmin Xie,
  • Wenhua Zhang

摘要

Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an improved YOLOv5 model. Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. Secondly, to broaden the image receptive field and improve feature fusion, a Receptive Field Block (RFB) model is incorporated into the neck network, replacing the original Spatial Pyramid Pooling Fast (SPPF) module. Additionally, a Lattice Structured Kernel (LSKBlock) attention mechanism is appended at the end of the neck network to further obtain richer semantic information. Finally, to flexibly improve the accuracy of bounding boxes of different sizes and enhance the robustness of the model, an \(\alpha CIOU\) loss function is utilized to replace the original Complete Intersection Over Union (CIOU) loss function. Experimental results demonstrate that the improved YOLOv5 model achieves a mean Average Precision (mAP) precision of 95.60%, with a parameter count of 18.36 M and a computational load of 30.10G, respectively. The Precision (P) and Recall (R) are 88.10% and 95.20%, providing strong support for deployment on mobile devices for real-time detection.