基于FEF-DeepLabV3+的电力金具锈蚀分割方法  

Segmentation method of power armor clamp corrosion based on FEF-DeepLabV3+

作　　者：王凌云[1] 李婷宜 李阳 万旭东 童华敏 Wang Lingyun;Li Tingyi;Li Yang;Wan Xudong;Tong Huamin(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China;State Grid Yichang Power Supply Company,Yichang 443000,China)

机构地区：[1]三峡大学电气与新能源学院,宜昌443002 [2]国网宜昌供电公司,宜昌443000

出　　处：《电子测量与仪器学报》2023年第7期166-176,共11页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(51907104)项目资助。

摘　　要：金具锈蚀在输电线路航拍图像中细节丰富且分布不规律,为克服分割检测过程中局部信息丢失、精度低和速度慢等问题,提出基于DeepLabV3+的金具锈蚀语义分割模型。替换其主干网络为轻量化改进MobileNetV3网络加快运算速度,并提出自适应特征金字塔(adaptive feature pyramid,AFP)结构融合多尺度。结合FRN层提出特征融合空洞空间金字塔池化(feature fusion atrous spatial pyramid pooling,FEF-ASPP)结构,能够在加强像素间联系的同时不降低分辨率。最后优化损失函数,提高算子的有效性。实验表明,mIoU和mPA分别达到了87.15%、96.64%,相较于原模型提高了3.09%、4.29%。参数量仅为原模型的48%,推理时间仅为15.94 ms,降低了对设备算力的要求,实现高效高精度、轻量化的输电设备锈蚀缺陷分割检测。The proportion of armor clamp rust in aerial images of power transmission lines is rich in details and irregularly distributed.To overcome problems such as local information loss,low accuracy,and slow speed in the segmentation detection process,a DeepLabV3+-based semantic segmentation model for armor clamp rust is proposed.The backbone network is replaced with a lightweight improved MobileNetV3 network to speed up computation,and an adaptive feature pyramid(AFP)structure is proposed to merge multiple scales.A feature fusion atrous spatial pyramid pooling(FEF-ASPP)structure is proposed,combined with the FRN layer to strengthen pixel relationships without reducing resolution.Finally,the loss function is optimized to improve the effectiveness of the operator.Experiments show that the mIoU and mPA reach 87.15%and 96.64%,respectively,which is an improvement of 3.09%and 4.29%compared to the original model.The parameter quantity is only 48%of the original model,and the inference time is only 15.94 ms,reducing the requirement for device computing power and achieving high-efficiency,high-precision,and lightweight segmentation detection of armor clamp rust in power transmission equipment.

关 键 词：深度学习 架空输电线路巡检 图像语义分割 缺陷检测 像素分类 

分 类 号：TM755[电气工程—电力系统及自动化] TP391[自动化与计算机技术—计算机应用技术]