# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results  
### Dataset  
[PERSON] et al. [36] collected and constructed a public dataset of insulators called Chinese Power Line Insulator Dataset (CPLID). This dataset includes normal insulator images and synthesized defective insulator images acquired via UAVs, showcasing diverse backgrounds of overhead insulators such as urban, riverine, field, and mountainous environments. Later, [PERSON] et al. [37] demonstrated the generalization capability of the proposed algorithm by means of stochastic affine transformation, Gaussian blurring, and lossless transformation to achieve further data enhancement for CPLID, which constructed the Unified Public Dataset for Insulators (UPID) with 6860 training and test images. [PERSON] et al. [38] enhanced the UPID dataset by using stochastic luminance and fog thickness, which constructed a new Synthetic Foggy Insulator Dataset (SFID), which contains 13,718 images. The dataset is more comprehensive and complex.  
However, there are still many shortcomings in the SFID dataset, because most of the insulators and defective objects in the SFID dataset are enhanced and synthesized, which are different from the real UAV inspection images, and the defective objects in SFID are  
Figure 5: Model architecture diagram of RML-YOLO.  
often much larger than those in the real aerial images, so it is difficult to adapt the trained model on the SFID dataset to the real scene of the inspection. Therefore, in this paper, insulator defect data obtained from real UAV inspection aerial photography are collected.  
In this paper, the dataset mainly uses DJI multi-rotor UAV series (such as DJI Royal 2, Royal 3, and DJI M300 RTK), unmanned helicopters, etc. By carrying dual-optical pods and other low-altitude image acquisition equipment to obtain image data of the power lines, we collected 563 pictures containing defective insulators, and labeled the data through LabellImg software, which contains multiple detection objects for each picture. Each picture in the dataset contains multiple object detection objects, and the object labeling is divided into two types: normal insulators and defective insulators. Randomly partition the dataset in an 8:2 ratio, and obtain an initial training set containing 450 images and an initial validation set containing 113 images. Then the data are enhanced based on the 563 pictures by random rotation, random luminance, rain, snow, fog, etc.; the pictures are scaled in equal proportions; the widest or the highest side of the scaled pictures is 640 pixels; and the enhanced pictures have 2250 pictures in the training set. After the picture, there are 2250 pictures in the training set and 452 pictures in the validation set data; we named this dataset as UID. Since the dataset collected in this paper originates from real detection scenarios, compared with other publicly available insulator datasets, the UID features complex object backgrounds and a high number of small objects, making this dataset more challenging. Figure 6 provides some UID dataset samples.  
The detailed information of the dataset is shown in Figure 7, where a represents the number of labels for normal and defective insulators in the dataset, with the number of normal insulators being much higher than the number of defective insulators; b represents the size of the bounding box, indicating that there are more small- and medium-sized target boxes; c represents the center coordinate distribution of the bounding box, indicating that the bounding box distribution is relatively uniform; and d represents the aspect ratio of the target relative to the entire image. It can be seen that the scattered points in the lower-left corner are denser, indicating that there are more small- and medium-sized targets in the dataset.  
Figure 6: UID dataset samples: the yellow box represents normal insulators, and the red box represents defective insulators.