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.