# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results
### SFID Dataset Comparison Experiment  
In order to further demonstrate the ability of the algorithm proposed in this paper for insulator detection, comparative experiments were conducted on the public insulator dataset SFID.  
The comparative results of general object detection algorithms on the SFID dataset are presented in Table 4. Due to the relatively large object objects in the SFID dataset and the absence of tiny objects, the detection difficulty is comparatively low. Consequently, YOLOv7-tiny, which performs well on small object detection, achieves an \(AP\) of only 76.02% on SFID. The \(AP50\) values across different models show minimal variation; however, the proposed RML-YOLO model remains superior to all other algorithms. On the SFID dataset, YOLOv8-n achieves the second-best \(AP50\) and \(AP75\), which are 0.32% and 1.18% lower than those of RML-YOLO, respectively. Faster R-CNN also performs notably well, achieving an \(AP\) of 83.1%, second only to RML-YOLO. However, Faster R-CNN comes with a significant drawback: its parameter count reaches 41.7 million, and its model size is 315 MB, which are several times larger than those of RML-YOLO. This substantial  
\begin{table}
\begin{tabular}{l 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 CACS-YOLO- & 57.7 & 81.9 & 66.3 & 50.2 & 64.8 & 59.9 & 2.64 & 55.5 \\ CACS-YOLO-m & 63.5 & 87.0 & 74.2 & 58.9 & 65.6 & 60.2 & 27.42 & 55.2 \\ LiteYOLO-ID-n & 49.0 & 76.6 & 54.9 & 40.6 & 56.2 & 51.8 & 0.94 & 2.14 \\ LiteYOLO-ID-m & 55.9 & 83.8 & 62.2 & 48.2 & 60.4 & 52.2 & 3.76 & 7.52 \\
**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}
Table 3: Comparison of experimental results for insulator defect detection algorithms on the UID dataset.  
computational demand makes Faster R-CNN unsuitable for deployment on resource-constrained devices.  
We also conducted comparisons with other algorithms that have performed well in the field of insulator defect detection. As shown in Table 5, it can be observed that CACS-YOLO has an \(AP\) that is 2.5% higher than that of RML-YOLO, and both models achieve an \(AP50\) of 99.3%. However, the number of parameters in CACS-YOLO is nearly eleven times that of RML-YOLO, and its model size is ten times larger. When comparing the lightweight n series models, it is evident that both CACS-YOLO and RML-YOLO have a model size of 5.5 MB, but the \(AP\) of CACS-YOLO-n is 3.4% lower than that of RML-YOLO. Meanwhile, although LiteYOLO-ID-n has a smaller parameter count and model size, its performance decreases correspondingly, with an \(AP\) that is 16.6% lower than that of RML-YOLO.  
Overall, whether on the highly challenging UID dataset constructed in this study or on the publicly available large-scale insulator defect dataset SFID, RML-YOLO demonstrates excellent performance, ensuring high detection accuracy while maintaining a lightweight design. This makes it particularly suitable for applications on resource-constrained devices, such as drones, for the detection of insulator defects.