# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results
### Comparative Experiments on Generalizability  
In order to demonstrate that the algorithm proposed in this paper has some generalization ability, comparative experiments are conducted on the open-source insulator dataset SFID and the UAV dataset VISDrone 2019. The VISDrone2019 dataset collects data from various drone cameras, including different cities, environments, and objects. Due to the effects of shooting angles, lighting, background, and other factors, detecting data from drones is more challenging than traditional computer vision tasks. The algorithm proposed in this paper will also be deployed on drones in the future; therefore, the VISDrone2019 dataset is chosen to further demonstrate the generality of the proposed algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & \(AP\) & \(AP50\) & \(AP75\) & \(AP_{\text{S}}\) & \(AP_{M}\) & \(AP_{L}\) & \
\begin{tabular}{c} **Param** \\ **(Million)** \\ \end{tabular} &
\begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} \\ \hline Faster-RCNN & 83.1 & 98.4 & 95.4 & 0 & **77.6** & 84.7 & 41.7 & 315 \\ YOLOv5-n & 80.37 & 98.6 & 96.0 & 0 & 67.5 & 82.3 & **2.5** & **5.3** \\ YOLOv7-tiny & 76.02 & 97.3 & 91.95 & 0 & 61.9 & 77.5 & 6.01 & 12.3 \\ YOLOv8-n & 81.36 & 98.98 & 96.26 & 0 & 68.9 & **85.4** & 3.0 & 6.3 \\ YOLOv9-t & 79.46 & 98.7 & 95.2 & 0 & 67.2 & 82.1 & 2.61 & 6.1 \\ YOLOv10-n & 81.0 & 98.5 & 96.0 & 0 & 68.4 & 83.1 & 2.69 & 5.8 \\ YOLOv11-n & 80.6 & 98.6 & 96.3 & 0 & 67.5 & 82.5 & 2.58 & 5.5 \\ Gold YOLO-n & 80.91 & 99.0 & 96.5 & 0 & 71.5 & 82.9 & 5.6 & 11.9 \\
**RML-YOLO (ours)** & **84.1** & **99.3** & **97.8** & 0 & 75.8 & 83.9 & 2.51 & 5.5 \\ \hline \hline \end{tabular}  
* The best results are shown in bold, and the second best result are indicated with underline.  
\end{table} 
Table 4: Comparison of experimental results for generic object detection algorithms on the SFID dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & \(AP\) & \(AP50\) & \(AP75\) & \(AP_{\text{S}}\) & \(AP_{M}\) & \(AP_{L}\) & \
\begin{tabular}{c} **Param** \\ **(Million)** \\ \end{tabular} &
\begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} \\ \hline CACS-YOLO-n & 80.7 & 98.8 & 96.5 & 0 & 68.6 & 83.1 & 2.64 & 5.5 \\ CACS-YOLO-m & **86.6** & **99.3** & **98.2** & 0 & **80.2** & **87.4** & 27.42 & 55.2 \\ LiteYOLO-ID-n & 67.5 & 95.0 & 80.5 & 0 & 51.6 & 67.9 & 0.94 & 2.14 \\ LiteYOLO-ID-m & 77.9 & 98.6 & 93.4 & 0 & 65.0 & 80.0 & 3.76 & 7.52 \\
**RML-YOLO (ours)** & 84.1 & **99.3** & 97.8 & 0 & 75.8 & 83.9 & 2.51 & 5.5 \\ \hline \hline \end{tabular}  
* The best results are shown in bold, and the second best result are indicated with underline.  
\end{table} 
Table 5: Comparison of experimental results for insulator defect detection algorithms on the SFID dataset. As can be seen from Table 6 experimental results, in terms of performance, RML-YOLO is a little bit weaker for large object detection, but it is in the lead in all other performance metrics. The algorithm proposed in this paper improves by 3.9% in AP compared with the base model YOLOV8 AP, 6.2% in AP50, 4.7% in AP75, 4.3% in detection accuracy for small objects, 3.9% for meso-scale objects, and 3.2% for large objects. RML-YOLO compares with YOLOv7-tiny with the second-best overall performance, AP compares with YOLOv7-tiny, and 3.2% for large objects. objects by 3.9%, and large-scale objects by 3.2%. RML-YOLO compares with YOLOv7-tiny, which has the next best overall performance, with 3.5% higher AP and 2.3% higher AP50 than YOLOv7-tiny. In addition, this paper also compares specialized object detection algorithms for UAV scenarios including HIC-YOLO proposed by [PERSON] et al. [41] and Drone-YOLO proposed by [PERSON] et al. [42].