# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results
### UID Dataset Comparison Experiment  
To demonstrate the superiority of the proposed algorithm, we conducted comparative experiments with several classic and state-of-the-art general-purpose object detection algorithms, including Faster R-CNN, YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and Gold-YOLO. The YOLO series offers various versions of differing sizes (e.g., n, s, m, l, x). Given our focus on deployment in resource-constrained environments, we uniformly selected the most lightweight version of each model for comparison. All experimental codes were sourced from official implementations: YOLOv5 and YOLOv8 were obtained from the same version of Ultralytics, while Faster R-CNN was obtained from MMDetection (version: faster-rcnn_r50_fpn_1x). To ensure the fairness of the comparative experiments, all models were trained and evaluated on the same dataset, using identical hardware and experimental environments, and adhering strictly to the official model specifications and experimental settings. As shown in Table 2, although YOLOv5-n has 0.01 million fewer parameters and a model size 0.2 MB smaller than the proposed RML-YOLO, RML-YOLO demonstrates superior detection performance across all metrics. Specifically, RML-YOLO achieves \(AP\), \(AP50\), and \(AP75\) values that are 7.45%, 4.87%, and 11.62% higher than those of YOLOv5-n, respectively, with \(AP_{S}\) being 9.7% higher. YOLOv7-tiny exhibits strong performance in small object detection, achieving an \(AP_{S}\) of 52.1%; however, this still falls 9.2% short of RML-YOLO. In terms of overall performance, YOLOv7-tiny ranks second to RML-YOLO. Compared with YOLOv7-tiny, RML-YOLO achieves improvements of 6.8%, 1.95%, and 10.82% in \(AP\), \(AP50\), and \(AP75\), respectively, despite having only 41.7% of YOLOv7-tiny parameters and 44.7% of its model size. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Models** & **R** & **M** & **L** & \(AP\) & \(AP50\) & \(AP75\) & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) & \
\begin{tabular}{c} **Param** \\ **(Million)** \\ \end{tabular} &
\begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} \\ \hline YOLOv8 & & & & 57.9 & 82.8 & 65.5 & 51.3 & 63.6 & 56.9 & 3.00 & 6.3 \\ +R & ✓ & & & 60.5 & 85.6 & 68.5 & 54.4 & 65 & 61.2 & 3.03 & 6.4 \\ +M & & ✓ & & 61.8 & 84.9 & 71.2 & 57 & 64.9 & 60.1 & 2.9 & 6.3 \\ +L & & & ✓ & 61.3 & 85.4 & 70.8 & 54.4 & 65.0 & 65.8 & **2.44** & **5.1** \\ +RL & ✓ & & ✓ & 62.7 & 87.0 & 72.3 & 54.7 & **6.6** & **67.1** & 2.47 & 5.2 \\ +RM & ✓ & ✓ & & 62.7 & 86.7 & 72.6 & 58.9 & 65.1 & 57.8 & 2.93 & 6.4 \\ +ML & & ✓ & ✓ & 62.9 & 86.6 & 73.4 & 58.6 & 63.8 & 59.8 & 2.48 & 5.4 \\ **RML-YOLO** & ✓ & ✓ & ✓ & **65.7** & **88.3** & **76.6** & **61.3** & 66.3 & 63.6 & 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 1: Ablation experiment results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & \(AP\) & \(AP50\) & \(AP75\) & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) & \
\begin{tabular}{c} **Param** \\ **(Million)** \\ \end{tabular} &
\begin{tabular}{c} **Size** \\ **(MB)** \\ \end{tabular} \\ \hline Faster-RCNN & 48.6 & 76 & 52.1 & 41.4 & 52.9 & 47.5 & 41.7 & 328 \\ YOLOv5-n & 58.25 & 83.43 & 64.98 & 51.6 & 64.4 & 56.1 & **2.5** & **5.3** \\ YOLOv7-tiny & 58.91 & 86.35 & 65.78 & 52.1 & 62.1 & 60.9 & 6.01 & 12.3 \\ YOLOv8-n & 57.88 & 82.81 & 65.53 & 51.3 & 63.6 & 56.9 & 3.0 & 6.3 \\ YOLOv9-t & 57.81 & 81.18 & 66.7 & 48.5 & 65.9 & **66.1** & 2.61 & 6.1 \\ YOLOv10-n & 56.5 & 80.9 & 65.04 & 48.9 & 63.8 & 54.2 & 2.69 & 5.8 \\ YOLOv11-n & 58.3 & 83.1 & 65.8 & 51.9 & 63.4 & 60.0 & 2.58 & 5.5 \\ Gold YOLO-n & 57.76 & 83.97 & 65.34 & 49.0 & 63.3 & 61.2 & 5.6 & 11.9 \\
**RML-YOLO (ours)** & **65.7** & **88.3** & **76.6** & **61.3** & **66.3** & 63.6 & 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}