摘要
现有绝缘子缺陷检测模型存在参数量大、推理速度慢等问题,难以满足电力巡检的实时性要求,同时无人机在巡检过程中面临雾霾、低照度等复杂环境干扰。针对以上问题,提出一种基于改进的轻量化YOLOv5s的绝缘子缺陷检测方法。该方法通过三重优化策略实现模型轻量化与检测精度之间的平衡:首先,利用轻量化模块对YOLOv5s网络进行深度优化,降低模型复杂度;其次,网络使用双向加权特征金字塔网络结构增强多尺度特征融合能力,并结合SIoU损失函数提升检测精度与收敛速度;最后,采用合成雾算法提升模型在复杂环境下的泛化性能。实验结果表明,所提模型参数、浮点运算数与模型存储大小仅为1.74×10^(6)、3.5×10^(9)与3.9 MB,mAP@0.5达到92.8%。
Aiming at the issues of large parameter count and slow inference speed,which is difficult to meet the demand for real-time online electric power inspection,and considering the complex environmental interferences such as the fog or low illumination during actual unmanned aerial vehicle inspection processes,an insulator defect detection method based on improved lightweight YOLOv5s is proposed.This method achieves a balance between model lightweighting and detection accuracy through a triple optimization strategy.Firstly,the YOLOv5s network is deeply optimized by using lightweight modules,which significantly reduces the model complexity.Secondly,the network adopts the bidirectional feature pyramid network structure to enhance the multi‑scale feature fusion capability,and combines the SIoU loss function to improve detection accuracy and convergence speed.Finally,the synthetic fog algorithm is employed to effectively enhance the generalization performance of the model in complex environments.Experimental results show that the parameters,floating point operands and size of the proposed model are only 1.74×10^(6),3.5×10^(9) and 3.9 MB,and the mAP@0.5 reaches 92.8%.
作者
姚春梅
席燕辉
YAO Chunmei;XI Yanhui(School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《湖南电力》
2025年第4期119-125,共7页
Hunan Electric Power
基金
国家自然科学基金项目(52277078)
湖南省自然科学基金项目(2022JJ30609)
湖南省研究生科研创新项目(QL20230212)。
关键词
轻量化
绝缘子缺陷检测
合成雾数据增强
航空图像
lightweight
insulator defect detection
synthetic fog data enhancement
aerial image
