基于改进YOLOv5的变电站典型缺陷自动检测算法  

Automatic Detection Algorithm for Typical Defects of Substation Based on Improved YOLOv5

作　　者：徐忠锴 刘艳玲 盛晓娟[3] 汪超 柯文俊 Xu Zhongkai;Liu Yanling;Sheng Xiaojuan;Wang Chao;Ke Wenjun(NR Electric Co.,Ltd.,Nanjing 211102,China;Chuxiong Power Supply Bureau of Yunnan Power Grid Co,Chuxiong 675000,China;China Aerospace Software Testing and Evaluation Center,Beijing 100854,China;School of Computer Science and Engineering,Southeast University,Nanjing 211189,China)

机构地区：[1]南京南瑞继保电气有限公司,江苏南京211102 [2]云南电网有限责任公司楚雄供电局,云南楚雄675000 [3]航天软件评测中心,北京100854 [4]东南大学计算机科学与工程学院,江苏南京211189

出　　处：《系统仿真学报》2024年第11期2604-2615,共12页Journal of System Simulation

基　　金：东南大学新进教师科研启动项目(RF1028623234)。

摘　　要：针对变电站缺陷识别场景下存在的复杂变电缺陷和样本不均衡等问题,提出一种改进的YOLOv5算法。向YOLOv5网络结构中引入Transformer模型,使用自注意力机制捕捉特征之间的长距离依赖关系。引入基于焦点损失的对损失函数进行优化,提高对小样本变电站缺陷的检测准确性和鲁棒性。为适应变电站缺陷识别任务,构建专门的数据集,采用聚类算法对真实标注框进行聚类,生成更准确的先验框。通过遗传算法选择适应该数据集的超参数,提升算法性能。实验结果表明,本文所提算法在变电站缺陷识别任务中取得了较好的性能表现。相较于传统的YOLOv5算法,改进算法在复杂变电缺陷和小样本目标的识别上具有更强的能力。In response to the challenges present in the context of defect recognition in substations,such as complex substation defects and sample imbalance,an improved YOLOv5 algorithm was proposed.The Transformer model was introduced into the YOLOv5 network structure,leveraging the self-attention mechanism to capture long-range dependencies among features.A focal loss-based optimization was employed to improve the loss function,as well as the detection accuracy and robustness of defects of small sample substations.To meet the requirements of substation defect recognition,a dedicated dataset was constructed.A clustering algorithm was applied to the real annotation boxes to generate more accurate prior boxes:The genetic algorithm was utilized to select hyperparameters that are specifically tailored to the dataset,further enhancing the algorithm's performance.Experimental results demonstrate that the proposed algorithm achieves favorable performance in substation defect recognition tasks.In comparison to the traditional YOLOv5 algorithm,the proposed algorithm exhibits superior capabilities in recognizing complex substation defects and small sample targets.

关 键 词：目标检测 YOLOv5 TRANSFORMER 自注意力机制 变电站缺陷识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]