###### Abstract  
Aiming at the application of the overhead transmission line insulator patrol inspection requirements based on the unmanned aerial vehicle (UAV), a lightweight ECA-YOLOX-Tiny model is proposed by embedding the efficient channel attention (ECA) module into the lightweight YOLOX-Tiny model. Some measures of data augmentation, input image resolution improvement and adaptive cosine annealing learning rate are used to improve the target detection accuracy. The data of the standard China power line insulator dataset (CPLID) are used to train and verify the model. Through a longitudinal comparison before and after the model improved, and a cross-sectional comparison with other similar models, the advantages of the proposed model are verified in terms of multi-target identification for normal insulators, localization for small target defect areas, and the parameters required for calculation. Finally, the comparative analysis between the proposed ECA-YOLOX-Tiny model and YOLOV4-Tiny model is given by introducing the visualization method of class activation mapping (CAM). The comparative results show that the ECA-YOLOX-Tiny model is more accurate in locating the self-explosion areas of defective insulators, and has a higher response rate for decision areas and some special backgrounds, such as the overlapping small target insulators, the insulators obscured by tower poles, or the insulators with high-similarity backgrounds. oe 6 August 202214 September 202214  
of two-stage algorithms, some candidate boxes are generated first, and their feature information is extracted, and then the position boxes are generated and the corresponding categories are predicted. The high detection precision and low detection speed are two difficult balancing characteristics of two-stage algorithms. The representative models of two-stage algorithms include R-CNN [5], spatial pyramid pooling networks (SPPNet) [6], fast R-CNN [7], and faster R-CNN [8], etc. On the contrary, the classification and regression localization are performed directly during the candidate boxes generated in the one-stage algorithms, and the low detection precision and high detection speed are two difficult balancing characteristics of one-stage algorithms. The representative models of one-stage algorithms include You Only Look Once (YOLO, V1-V5 versions) [9; 10; 11; 12; 13] and single shot multibox detector (SSD) [14], etc. The related applications are given as follows: the CME-CNN was used firstly by [PERSON] et al. to eliminate complex background information and then the Exact R-CNN was used to detect insulator defects [15]. The faster R-CNN and SSD-MobileNet were used by [PERSON] et al. to identify grapes as well as grape leaves diseases [16]. The improved YOLOV5 was used by [PERSON] et al. to conduct real-time detection for robot apple-picking [17]. Based on the application requirements and continuous development of deep learning, a number of lightweight models are emerging, the lightweight classification models include SqueezeNet [18], ShuffleNet [19], MobileNet, and MnasNet, etc., and the lightweight target detection models include YOLOV3-Tiny [20] and YOLOV4-Tiny, etc. The related applications are given as follows: the lightweight MnasNet was used to replace the feature extraction network of SSD by [PERSON] et al. and the improved ISSD network was used to detect insulators and spacers [21]. The lightweight MobileNet was adopted to improve the YOLOV4 model by [PERSON] et al. and the defect of insulator images after Laplace sharpening was detected [22]. The lightweight YOLOV4-Tiny was used by [PERSON] et al. to detect birds associated with transmission line faults [23]. In 2021, the YOLOX algorithm was proposed by [PERSON] et al. which improved the detection accuracy and reduced the model size compared with other similar algorithms in the YOLO family [24]. In practical applications, the computational capability of UAV is a limiting factor.