电瓷避雷器Issue(6)：187-195,9.DOI:10.16188/j.isa.1003-8337.2023.06.022
融合多尺度特征的轻量级YOLOv7绝缘子缺陷检测算法
Detection Algorithm of Lightweight YOLOv7 Insulator Defect Based on Multi-scale Feature Fusion
摘要
Abstract
A lightweight YOLOv7 insulator defect detection algorithm that integrates multi-scale features is proposed to address the issues of imbalanced detection accuracy and speed in current insulator defect detection algorithms,as well as poor detection performance for small target insulator defects.Using YOLOv7 as the basic framework and CA HostNet as the backbone network;Replace the residual convolu-tion in the head prediction network with deep separable convolution;Design a Light-SPPCSPC feature extraction module in the neck network;In the feature pyramid section,fuse feature maps of different scales.The experimental results show that the proposed algorithm achieves a balance between accuracy and speed,reducing the missed detection rate of insulator defects关键词
绝缘子缺陷/轻量化/空间金字塔/多尺度特征融合Key words
insulator defects/lightweight/pyramid of space/multi-scale feature fusion引用本文复制引用
党宏社,许勃,张选德..融合多尺度特征的轻量级YOLOv7绝缘子缺陷检测算法[J].电瓷避雷器,2023,(6):187-195,9.基金项目
国家自然科学基金项目(编号:61871206) （编号:61871206）
陕西省科技厅自然科学基金项目(编号:2020JM-509).Project supported by National Natural Science Foundation of China(NSFC)(No.61871206) （编号:2020JM-509）
Natural Science Foundation of Science and Technology Department of Shaanxi Province(No.2020JM-509). （No.2020JM-509）
