Scholarlink

<p>To address the challenges of high parameter volume, insufficient detection accuracy, and high false positive rates in traditional insulator target detection algorithms, this paper proposes a lightweight detection algorithm based on an enhanced YOLOX framework. First, Depthwise Separable Convolutions (DWConv) are employed to replace traditional convolutions, simplifying the model structure and reducing parameter count. Second, the Spatial Pyramid Pooling Fast-N (SPPF-N) module is introduced to substitute the traditional SPP module, and the last CSPLayer is removed, further compressing parameters while improving detection speed. Meanwhile, an Enhanced Convolutional Block Attention Module (E-CBAM) is embedded between the backbone and neck networks to significantly enhance feature extraction capabilities, overcoming the performance limitations of lightweight networks. Additionally, Coordinate Attention (CA) is integrated into the detection head to improve the precision of small target detection. Finally, the σWIoU loss function replaces the traditional IoU to accelerate convergence and optimize overall performance. Experiments show that compared to the original YOLOX, the proposed algorithm reduces parameters by 53.41% to 4.164&#xa0;M and computational load to 12.975G, with mAP increased by 1.3%, detection accuracy reaching 98.81%, and recall achieving 100%. This algorithm balances high precision with lightweight design, meeting mobile deployment requirements and providing an efficient and reliable solution for insulator detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Lightweight insulator target detection algorithm based on improved YOLOX

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

查看全文
  • Bing Zeng,
  • Wei Hua,
  • Dezhi Li,
  • Zhihao Zhou,
  • Hao Wan,
  • Yunmin Xie,
  • Tangbing Li,
  • Yucong Chen,
  • Jianglei Li,
  • Shenli Wang,
  • Shixun Fu,
  • Zihan Jin,
  • Wenhua Zhang

摘要

To address the challenges of high parameter volume, insufficient detection accuracy, and high false positive rates in traditional insulator target detection algorithms, this paper proposes a lightweight detection algorithm based on an enhanced YOLOX framework. First, Depthwise Separable Convolutions (DWConv) are employed to replace traditional convolutions, simplifying the model structure and reducing parameter count. Second, the Spatial Pyramid Pooling Fast-N (SPPF-N) module is introduced to substitute the traditional SPP module, and the last CSPLayer is removed, further compressing parameters while improving detection speed. Meanwhile, an Enhanced Convolutional Block Attention Module (E-CBAM) is embedded between the backbone and neck networks to significantly enhance feature extraction capabilities, overcoming the performance limitations of lightweight networks. Additionally, Coordinate Attention (CA) is integrated into the detection head to improve the precision of small target detection. Finally, the σWIoU loss function replaces the traditional IoU to accelerate convergence and optimize overall performance. Experiments show that compared to the original YOLOX, the proposed algorithm reduces parameters by 53.41% to 4.164 M and computational load to 12.975G, with mAP increased by 1.3%, detection accuracy reaching 98.81%, and recall achieving 100%. This algorithm balances high precision with lightweight design, meeting mobile deployment requirements and providing an efficient and reliable solution for insulator detection.