内蒙古民族大学学报（自然科学版）2026，Vol.41Issue(4)：59-67,9.DOI:10.14045/j.cnki.15-1220.2026.04.008
基于改进YOLOv5s的高压柜目标检测算法
Improved YOLOv5s-Based Object Detection Algorithm for High-Voltage Switchgear
摘要
Abstract
To address the issues of background interference in power equipment images and inaccurate detection of keyholes and indicator lights in robotic vision,this paper proposes an improved YOLOv5s-based object detection algo-rithm for high-voltage switchgear.By replacing standard convolutions with deformable linear convolutions,the algorithm enhances feature extraction for targets such as keyholes.Additionally,a context anchor attention module is introduced to capture long-range contextual information,thereby improving the model's feature representation.To further improve localization accuracy under uneven data quality,a bounding box loss function based on a dynamic non-monotonic focus-ing mechanism is adopted.Experimental results on a self-built substation high-voltage switchgear dataset show that the improved model outperforms the original YOLOv5s.The mAP@50 reaches 80.01%,an improvement of 1.2%,while the mAP@50:0.95 reaches 43.67%,an improvement of 12.4%.These results demonstrate that the proposed method offers better adaptability and higher detection accuracy for high-voltage switchgear detection tasks.关键词
目标检测/YOLOv5s/高压柜检测/注意力机制/线性可变形卷积/WIoUKey words
object detection/YOLOv5s/high-voltage cabinet inspection/attention mechanism/LDConv/WIoU分类
信息技术与安全科学引用本文复制引用
边慧龙,杜娟娟,赵旭东,吴晓强..基于改进YOLOv5s的高压柜目标检测算法[J].内蒙古民族大学学报（自然科学版）,2026,41(4):59-67,9.基金项目
内蒙古自治区重点研发与成果转化计划项目(2023YFDZ0043) （2023YFDZ0043）
