# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results
### Visual Comparative Experiment  
In addition, in Figure 10, this article compares the detection performances of mainstream object detection algorithms on an UID dataset. The figure shows various scene combinations, including noise, complex background, low light, rainy and snowy days, small objects, occlusion, etc. In Figure 10a, the main challenges are noise interference and complex background. From the visualization of detection results, it can be observed that RML-YOLO, LiteYOLO-ID, CACS-YOLO, YOLOv8, and Faster-RCNN successfully detected all objects. However, both YOLOv7 and Gold-YOLO exhibited false positives; YOLOv7 falsely identified weeds in the background as normal insulators and misclassified a normal insulator in the top-right corner as a defective one, failing to recognize both normal and defective insulators against the complex left background. Gold-YOLO successfully detected defective insulators but missed the normal insulator on the left and falsely detected background interference as normal insulators. YOLOv9 did not detect any objects in this image. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & \(AP\) & \(AP50\) & \(AP75\) & \(AP_{\text{S}}\) & \(AP_{M}\) & \(AP_{\text{L}}\) & \
\begin{tabular}{c} **Param** \\ **(Million)** \\ \end{tabular} &
\begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} \\ \hline Faster-RCNN & 9.6 & 16.3 & 9.9 & 2.3 & 20.1 & 29.1 & 41.7 & 347 \\ YOLOv5-n & 18.5 & 31.2 & 18.7 & 9.2 & 28 & **42.9** & **2.5** & **5.3** \\ YOLOv7-tiny & 18.9 & 34.8 & 17.8 & 10.7 & 27.7 & 37.7 & 6.01 & 12.3 \\ YOLOv8-n & 18.5 & 30.9 & 18.7 & 9.5 & 28.2 & 37.4 & 3.0 & 6.3 \\ YOLOv9-t & 18.7 & 30.8 & 19.2 & 8.9 & **28.9** & **42.9** & 2.61 & 6.1 \\ YOLOv10-n & 18 & 30.1 & 18.4 & 8.8 & 27.8 & 39.3 & 2.69 & 5.8 \\ YOLOv11-n & 18.6 & 31.1 & 19.0 & 9.4 & 28.2 & 38.8 & 2.58 & 5.5 \\ Gold YOLO-n & 18.4 & 31.1 & 18.7 & 9.2 & 28 & 42 & 5.6 & 11.9 \\ CACS-YOLO & 18.4 & 31.0 & 18.9 & 9.2 & 28.1 & 40.9 & 27.42 & 55.2 \\ LiteYOLO-ID & 13.0 & 24.5 & 12.4 & 7.0 & 19.9 & 26.2 & 3.76 & 7.52 \\ HIC-YOLO & 19.3 & 32.8 & 19.6 & **14.6** & 24.2 & 18.6 & 2.35 & 5.4 \\ Drone-YOLO & 20.3 & 33.3 & 21.0 & 12.0 & 29.2 & 40.1 & 2.93 & 6.3 \\
**RML-YOLO (ours)** & **22.4** & **37.1** & **23.4** & 13.8 & **32.1** & 40.6 & 2.51 & 5.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative experiment on VISDrone 2019. In Figure 10b, the main challenges include complex backgrounds and small object. Ultimately, only RML-YOLO, YOLOv10, and CACS-YOLO successfully identified all object without false detections. Faster-RCNN, YOLOv7, YOLOv9, and Gold-YOLO all produced false positives by misclassifying background weeds as insulators. In Figure 10c, the key challenges are low illumination, partial occlusion, and small object. Due to partial occlusion affecting the second insulator from the right, Faster-RCNN, YOLOv8, YOLOv10, and LiteYOLO-ID failed to detect this partially occluded object. Only RML-YOLO, YOLOv5, and CACS-YOLO detected all object, though YOLOv5 yielded a low confidence score (0.27) for the occluded object. In Figure 10d, the main challenges are random rain interference and complex backgrounds. Faster-RCNN and YOLOv10 failed to detect defective insulators. YOLOv10 misclassified a normal insulator on the left, affected by random rain as a defective one. While YOLOv7 detected all object, it mistakenly identified leaves on the right as normal insulators. In Figure 10e, the challenges are small targets and complex backgrounds. The defective insulator is on the left, located at the image edge with indistinct features and susceptible to interference from surrounding wires. Only RML-YOLO correctly identified the object. The defective insulator above was more apparent: hence, all algorithms successfully recognized it. In Figure 10f, challenges arise from occlusion and complex backgrounds. The left insulator and its defective region are partially occluded. Only RML-YOLO and Faster  
Figure 10: Visual comparison of the detection results of different algorithms.