## 4 Case Verification and Analysis
### The Analysis of Detection Effect before and after the Model Improved  
The original YOLOX-Tiny model with an input image resolution of 416 \(\times\) 416 is denoted as YOLOX-Tiny_416, the YOLOX-Tiny model with 640 \(\times\) 640 image resolution is denoted as YOLOX-Tiny_640, the model with 640 \(\times\) 640 image resolution and embedded ECA lightweight attention module is denoted as ECA-YOLOX-Tiny, and the ECA-YOLOX-Tiny model with adaptive cosine annealing learning rate is denoted as ECA-YOLOX-Tiny (ACLR). The training results of different models are shown in Table 3, where \(TT\) represents the training time of the model. It can be seen from Table 3 that the accuracy indexes (\(mAP\), \(P_{r}\), \(R_{e}\) and \(F1\) score) of low input image resolution are lower than those of high input image resolution, but the speed indexes (\(TT\), \(FPS\)) of low input image resolution is better than those of high input image resolution. Compared with YOLOX-Tiny_640, the \(mAP\) can be improved and obtained equivalent detection values of \(P_{r}\), \(R_{e}\) and \(F1\) score with the introduction of the lightweight ECA module. The training pattern of decaying learning rate with equal intervals of \"_step_\" is adopted in YOLOX-Tiny_416, YOLOX-Tiny_640 and ECA-YOLOX-Tiny, and the training strategy of adaptive cosine annealing learning rate is employed in ECA-YOLOX-Tiny (ACLR). Comparing the two training methods, the accuracy indexes (\(mAP\), \(P_{r}\), \(R_{e}\) and \(F1\) score) of ECA-YOLOX-Tiny (ACLR) have been improved by different degrees, and the speed indexes (\(TT\), \(FPS\)) of ECA-YOLOX-Tiny (ACLR) can be accepted for UAV detection application although these indexes decreased at a certain level. Figure 8: The \(F1\) score curve of defective and normal insulators: (**a**) \(F1\) score curve of defective insulators, (**b**) \(F1\) score curve of normal insulators. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & _mAP (\%)_ & \(P_{r}\) (\%) & \(R_{e}\) (\%) & \(F1\) (\%) & \(TT\) (b) & \(FPS\) (**Pictures s\({}^{-1}\)**)** \\ \hline YOLOX-Tiny\_416 & 99.54 & 98.525 & 98.6 & 98.5 & 82.5 & 4.89 \\ YOLOX-Tiny\_640 & 99.61 & 98.825 & 98.97 & 99.0 & 175.0 & 2.17 \\ ECA-YOLOX-Tiny & 99.72 & 98.82 & 98.895 & 99.0 & 198.5 & 2.17 \\ ECA-YOLOX-Tiny (ACLR) & 99.94 & 98.98 & 99.63 & 99.5 & 215.0 & 2.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The training results of different model. The pictures in Table 4 show the detection effect of the three models in different weather environments, backgrounds, brightness and angles, respectively. It can be seen from the pictures in Table 4 that the ECA-YOLOX-Tiny model shows a much better detection accuracy for small target defect areas in different weather, backgrounds, brightness and angle conditions, and the model generalization ability and robustness are good. The pictures in Table 5 show the detection effect of the three models for multi-scale and multi-target normal insulators in different backgrounds. The following conclusions can be obtained by comparative analysis: the detection accuracy for multi-scale insulators in all of the three models is high when the insulators occupy a larger proportion in an image, and the detection accuracy is low in the inverse conditions. Compared with the other two models, the overall performance of the ECA-YOLOX-Tiny model is the best and achieves the highest comprehensive detection accuracy, a perfect goodness-of-fit between the predicted bounding boxes and insulators, and almost no missed detection. The ECA-YOLOX-Tiny shows powerful target recognition and localization ability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ECA-YOLOX-Tiny** & **YOLOX-Tiny\_640** & **YOLOX-Tiny\_416** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The detection results of defective insulators before and after the model improved.