# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 1 Introduction  
With the acceleration of industrialization, the demand for electric power continues to grow, and higher requirements are put forward for the reliability, safety, and stability of transmission lines. As critical components in power infrastructure, insulators perform the dual functions of mechanical support and electrical insulation. Subjected to prolonged exposure to extreme environmental conditions, these components face degradation risks, including spontaneous fracturing and surface flashover. Such failures will not only trigger power outages, but also may lead to conductor damage, fire, and other serious consequences, resulting in huge economic losses and security risks. Therefore, the regular inspection of insulators is absolutely necessary to ensure the stable operation of the power grid [1, 2, 3, 4]. The traditional manual inspection method faces problems such as complicated terrain, low efficiency, and high personal risk [5]. In recent years, with the development of dronesand computer vision technology [6], the use of drones carrying optical sensors to obtain images, and then through the vision algorithm to identify insulator defects, has gradually become the mainstream method [7; 8; 9; 10]. Nowadays, with the continuous development of computer vision, a variety of computer vision-based methods have been applied to insulator image detection over the past few years, including image-processing-based and deep-learning-based ones, achieving remarkable results. Traditional methods such as Histogram of Oriented Gradients (HOG) [11], Scale-Invariant Feature Transform (SIFT) [12], and Local Binary Patterns (LBPs) [13] rely on manual feature design, making them susceptible to shooting angles and lighting changes. Consequently, they have poor generalization ability and low detection accuracy. In contrast, deep-learning-based object detection methods like Faster R-CNN [14], SSD [15], and the YOLO series [16; 17; 18; 19; 20; 21; 22; 23; 24] offer higher robustness and detection performance. While two-stage algorithms boast high accuracy, their computational complexity is significant. In comparison, single-stage algorithms, with their lighter structure and faster speed, are more suitable for deployment on resource-constrained drone platforms. Although the above research work has addressed some of the difficulties in inspecting insulators by UAVs to a certain extent, there are still more challenges that have not been effectively addressed. Most of these problems arise from the practical engineering applications to which they are oriented. The significant variation in insulator and defect sizes poses a major challenge to conventional detection algorithms. Drones, equipped with high-precision cameras, capture high-resolution images from afar, making the objects seem quite small relative to the overall image. Moreover, variations in shooting distance and angle further amplify these size discrepancies, thus undermining the effectiveness of standard detection methods. In existing research on power line insulator defects, a strategy of constructing a multi-scale fusion network is typically employed for multi-scale objects. [PERSON] et al. [25] integrated an scSE attention mechanism with PA-Net to address multi-scale detection challenges. However, their approach merged features from only two levels and utilized numerous lightweight Ghost modules, which lessened the model's overall detection performance. In contrast, [PERSON] et al. [26] put forward ResPANet, a multi-scale feature fusion framework that leverages residual jump connections to integrate high- and low-resolution features, thereby improving multi-scale detection capability without adding to the computational cost. Despite this progress, the method did not incorporate a 160 \(\times\) 160 feature map that contains rich information, limiting its effectiveness for small objects. Similarly, [PERSON] et al.