GER-YOLO Fault-Detection Algorithm for Transmission-Line Insulators
A novel algorithm named GER-YOLO for insulator defect detection is proposed to address the issues of large algorithm parameters,complex image backgrounds,and significant insulator-scale changes in the unmanned-aerial-vehicle detection of insulator defects.First,GhostNetV2 is used to construct the C2fGhostV2 module,which significantly reduces the number of parameters and computation while maintaining the algorithm's detection accuracy.Second,an efficient multi-scale attention(EMA)network with cross-spatial-learning ability is introduced,which enables the complete mining of feature information and suppresses meaningless information.Finally,the C2fRFE module is proposed to capture long-range information,learn multiscale features,and improve the detection ability of insulators and their defects at different scales.Experimental results show that compared with the baseline model YOLOv8s,GER-YOLO offers a higher mean average precision(mAP)by 1.1%,reduces the parameter and computational costs by 30.2%and 31.0%,respectively,and can effectively detect insulator defects.
insulator defect detectionlightweightattention mechanismmultiscale information
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