广东工业大学学报2026，Vol.43Issue(2)：81-90,10.DOI:10.12052/gdutxb.240174
基于AAGV-YOLOX模型的输电线路异物检测研究
Research on Foreign Object Detection in Power Transmission Lines Based on the AAGV-YOLOX Model
摘要
Abstract
To address the common issues of false and missed detections in the current process of foreign object detection on power transmission lines,as well as the limited detection accuracy due to the variability in the size of foreign objects,this paper proposes an AAGV-YOLOX model for the detection of foreign objects on power transmission lines.The model first designs an Adaptive Dilated Convolution(Adaptive Dilated Convolution,ADConv)and constructs a feature extraction module(Adaptive Dilated Convolution Module,ADCM)to effectively distinguish the widely distributed foreign objects from background information,thereby enhancing the model's feature extraction capabilities.Subsequently,an Adaptive Receptive Field Feature Fusion(Adaptive Receptive Field Feature Fusion,ARFFF)module is introduced into the neck network to fully integrate features of different scales,further improving detection accuracy.Finally,the GVFL loss function is proposed,which not only increases the convergence speed of the proposed network but also enhances the localization accuracy.Experimental results show that the average precision mean of this model on the self-built dataset of foreign objects on power transmission lines reaches 90.34％,with a 5.56 percentage points improvement over the YOLOXs,demonstrating the effectiveness of the proposed method in improving the detection of foreign objects on power transmission lines.关键词
输电线路/异物检测/You Only Look Once version X(YOLOX)Key words
transmission lines/foreign object detection/You Only Look Once version X(YOLOX)分类
信息技术与安全科学引用本文复制引用
谢国波,夏炜,林志毅,谢建辉,刘汉林,余意..基于AAGV-YOLOX模型的输电线路异物检测研究[J].广东工业大学学报,2026,43(2):81-90,10.基金项目
国家自然科学基金资助项目(62076073) （62076073）
