Enhancing hyperspectral power transmission line defect and hazard identification with an improved YOLO-based model  

作　　者：WANG Meng SUN Long JIANG Jiong YANG Jinsong ZHANG Xingru 

机构地区：[1]Ningbo Power Transmission and Transformation Construction Co.,Ltd.,Yongyao Technology Branch,Ningbo 315100,China [2]State Grid Ningbo Power Supply Company,Ningbo 315100,China

出　　处：《Optoelectronics Letters》2024年第11期681-688,共8页光电子快报（英文版）

摘　　要：To address the challenges of inefficient manual inspections and time-consuming video monitoring for power transmission lines,this paper presents an innovative solution.It combines deep learning algorithms with visible light remote sensing images to detect defects and hazards.Deep learning offers enhanced robustness,significantly improving efficiency and accuracy.The study utilizes you only look once version 7(YOLOv7)as a foundational framework,enhancing it with the Transformer algorithm,Triplet Attention mechanism,and smooth intersection over union(SIoU)loss function.Experimental results show a remarkable 92.3%accuracy and an 18.4 ms inference speed.This approach promises to revolutionize power transmission line maintenance,offering real-time,high-precision defect and hazard identification.

关 键 词：DEFECT UNION utilize 

分 类 号：TM75[电气工程—电力系统及自动化] TP391.41[自动化与计算机技术—计算机应用技术]