## 4 Case Verification and Analysis
### The Analysis of Detection Effect before and after the Model Improved  
To further verify the advantages of lightweight ECA-YOLOX-Tiny, the similar models of lightweight YOLOV4-Tiny and SSD-MobileNet are selected for comparative analysis, and the parameter settings of the three models are kept consistent. The training results of the three models are shown in Table 6. It can be seen from Table 6 that the indexes of \(mAP\), \(R_{e}\) and \(F1\) of ECA-YOLOX-Tiny are significantly improved compared with YOLOV4-Tiny and SSD-MobileNet, the \(P_{r}\) of ECA-YOLOX-Tiny is higher than YOLOV4-Tiny but lower than SSD-MobileNet, the number of parameters in ECA-YOLOX-Tiny is the lowest. In terms of detection accuracy and parameter number indexes, ECA-YOLOX-Tiny has the obvious advantage. In addition, caused by the high resolution of input images, the training time \begin{table}
\begin{tabular}{c c c} \hline
**ECA-YOLOX-Tiny** & **YOLOX-Tiny\_640** & **YOLOX-Tiny\_416** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The multi-target detection results of normal insulators before and after the model improved. and _FPS_ of ECA-YOLOX-Tiny is lower than the other two models. The model training is carried out offline and has no effect on the real-time UAV defect detection, and the training time problem can be solved by improving the hardware condition of training device. In practical application, the detection speed of 2.10 _FPS_ meets the real-time requirements of the UAV defect detection for insulators. Table 7 provides the detection results of the three models on the test set. Compared with the other two models, the detection accuracy (_Acc_) of the ECA-YOLOX-Tiny model is obviously higher than the other two models, and the number of missed detections and false detections is the lowest. The cost of the detection accuracy improvement, however, is the long testing time. The detection effect pictures of three models for defective insulators with different complex backgrounds are shown in Table 8. In terms of insulator defect detection, ECA-YOLOX-Tiny has the highest detection accuracy, although the accuracy is only slightly lower than YOLOV4-Tiny, the final predicted bounding boxes of ECA-YOLOX-Tiny are more consistent with the real insulator boundaries and the location of the predicted bounding boxes for insulator defect are more accurate. Compared with SSD-MobileNet, the ECA-YOLOX-Tiny model has almost no missed or false detection for insulator defects in different complex backgrounds, while SSD-MobileNet has missed detections for insulator defects with background interference and images rotated 180\({}^{\circ}\). The powerful location and identification abilities of ECA-YOLOX-Tiny for small target defective insulators were verified by analyzing the pictures in Table 8. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Backbone** & _mAP_ (\%) & \(P_{r}\) (\%) & \(R_{e}\) (\%) & \(F1\) (\%) & \
\begin{tabular}{c} **Parameters** \\ **(10\({}^{6}\))** \\ \end{tabular} &
\begin{tabular}{c} **Training** \\ **Time (h)** \\ \end{tabular} & **FPS (Pictures s\({}^{-1}\))** \\ \hline ECA-YOLOX-Tiny & CSPDarknet & 99.94 & 98.98 & 99.63 & 99.5 & 5.1 & 215.0 & 2.10 \\ YOLOV4-Tiny & CSPDarknet53-Tiny & 98.75 & 95.93 & 96.12 & 96.0 & 5.9 & 64.5 & 4.87 \\ SSD-MobileNet & MobileNetV1 & 96.95 & 99.905 & 62.77 & 76.0 & 6.4 & 52.5 & 6.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The training results of similar network models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Test Images** & **Successful Detection** & **Missed Detection** & **False Detection** & _Acc_ (\%) & **Test Time (s)** \\ \hline ECA-YOLOX-Tiny & 584 & 578 & 6 & 0 & 98.97 & 418 \\ YOLOV4-Tiny & 584 & 561 & 18 & 5 & 96.06 & 127 \\ SSD-MobileNet & 584 & 371 & 213 & 0 & 63.53 & 122 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The detection results of different models on the test set.