## 5 Conclusions  
We took UAV as the carrier to achieve inspection and self-explosion defect detection for overhead transmission line insulators. Based on the lightweight YOLOX-Tiny model, the measures of lightweight attention module, data augmentation, input image resolution improvement, adaptive cosine annealing learning rate, and class activation mapping (CAM) visualization tool are introduced, and the lightweight ECA-YOLOX-Tiny model is proposed in the paper. The actual inspection insulators' case database is used to comprehensively verify and analyze the model performance, and the following conclusions are drawn. 1. Aiming at the problem of insufficient defective samples in the insulator data set, the original data are enhanced by various methods according to the flight shooting conditions of UAV. Through the data enhancement measures, the generalization capability of the network is improved and the characteristic information of the insulators can be fully learned by the model, and then the detection accuracy of the model is improved for small target defective regions and multi-target insulators with different scales. 2. The higher resolution images are adopted as the network model inputs, so that the feature information of the insulator defect regions as well as small target insulator strings can be better learned by the network model. The examples prove that the detection accuracy indexes of _mAP_, _P\({}_{r}\)_, _R\({}_{e}\)_ and _F1_ score are improved after the resolution of the input images is improved from 416 \(\times\) 416 to 640 \(\times\) 640. 3. By introducing the lightweight attention module (ECA) in the ECA-YOLOX-Tiny model, the semantic information of the feature maps is further enhanced, the nonlinear capability of the network is improved, and the redundant information is reduced. Compared with YOLOX-Tiny_640 model, the _mAP_ of the ECA-YOLOX-Tiny model has been improved by 0.11% due to the introduction of the ECA module, and the _mAP_ is further improved by 0.22% when the model training method of equal interval \"_step_\" decay learning rate is replaced by the adaptive cosine annealing learning rate. Compared with the similar models YOLOV4-Tiny and SSD-MobileNet, the _mAP_ of ECA-YOLOX-Tiny has been improved by 1.19% and 2.99%, respectively, and the number of parameters of the ECA-YOLOX-Tiny model can be compressed to best meet the hardware conditions of the UAV. 4. With the improvement of detection accuracy, the speed indexes of training time and _FPS_ value are certainly decreased, however, it is necessary to improve the model detection accuracy at the cost of less training time and _FPS_ value in the face of high precision detection requirements. The testing results show that the detection speed of the ECA-YOLOX-Tiny model reached 2.10 pictures s\({}^{-1}\), which basically meets the requirements of real-time insulator detection. 5. In order to further verify the superiority of the ECA-YOLOX-Tiny model, the class activation mapping is introduced to visualize the prediction results of different models. Compared with the YOLOV4-Tiny model, the ECA-YOLOX-Tiny model is more accurate in locating the self-explosion areas of defective insulators and has a higher response degree for decision areas. Moreover, the ECA-YOLOX-Tiny model has a higher response to complex backgrounds of overlapping small target insulators, insulators obscured by tower poles, and high similarity background insulators, etc. \begin{table}
\begin{tabular}{c c} \hline \hline
**ECA-YOLOX-Tiny** & **YOLOV4-Tiny** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Cont. **Author Contributions:** Conceptualization, C.R. and S.Z. ; methodology, C.R. and S.Z. ; software, C.Q. ; validation, C.R. and S.Z. ; formal analysis, Z.Z. ; investigation, Z.Z. and C.Q. ; resources, S.Z. ; data curation, C.R. and S.Z. ; writing--original draft preparation, C.R. and S.Z. ; writing--review and editing, C.R., S.Z. and C.Q. ; supervision, Z.Z. ; visualization, C.R. ; project administration, S.Z. ; funding acquisition, S.Z.