# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 1 Introduction
[27] concentrated on large-scale feature maps through a Bi-PAN-FPN approach, enhancing the frequency and duration of multi-scale feature fusion to lower false and missed detections in aerial images. As can be seen from the above literature, employing the method of fusing features of different levels can boost the detection ability for multi-scale objects to a certain extent. These detection methods offer references for multi-scale object detection. Current deep learning implementations face a critical efficiency-accuracy tradeoff, where computational complexity escalates alongside precision enhancement, severely impeding deployment feasibility on edge devices like UAVs with constrained resources. To address this challenge, [PERSON] et al. [25] developed a context-streamlined convolutional framework (C2 fGhost) that achieves 75.7% parametric compression through architectural pruning. [PERSON] et al. [28] developed a lightweight insulator feature pyramid network (LIFPN) alongside a compact insulator-oriented detection head (LIHead) to trim model parameters. Aiming to preserve accuracy while drastically cutting parameters, [PERSON] et al. [29] created a new lightweight convolution module, EGC; built the backbone network around it; and employed a lightweight EGC-PANet in the neck section for efficient feature fusion and further parameter reduction. From the above reference, it is evident that the mainstream methodsfor reducing parameter volume generally involve modifications to network architecture, often at the expense of some degree of accuracy. The detection accuracy of insulators is often limited by complex backgrounds and insufficient feature extraction capabilities.The extensive distribution of high-voltage power lines, along with varied terrain, causes a high degree of resemblance between objects and their backgrounds, posing a tough challenge for detection. [PERSON] et al. [30] proposed a cascaded structure as the backbone network, yet this increased model complexity considerably. [PERSON] et al. [31] enhanced the Faster R-CNN model by adding a feature pyramid network (FPN) to identify insulators in complex-background images. Nevertheless, combining the two-stage Faster R-CNN model with the FPN network further complicates the model, creating difficulties in both training and detection. The referenced studies provide a foundation for our research. While existing detection algorithms address some challenges in drone-based insulator inspection, they fall short in handling issues like significant object size variation and high parameter volume. To address these challenges, this paper proposes RML-YOLO, a lightweight, multi-scale insulator defect detection method for drone-captured images. The core innovations are MSFENet and LMSHead, which enhance multi-scale object detection while reducing model parameters. The key contributions are as follows:  
* To address the challenges posed by the large variation in insulator and defect sizes, we propose MSFENet. This network improves upon traditional FPN by proposing a TSFF module to better fuse features across different layers. A multi-head detection strategy is employed to enhance the model's ability to detect multi-scale objects. * To mitigate the issue of high parameter volume in existing detection algorithms, we propose LMSHead, a lightweight multi-scale shared detection head. This head utilizes partial convolutions to extract features from various levels and applies shared convolutions to reduce model size and parameter count, while addressing the uneven distribution of objects across scales. * To improve detection performance in complex backgrounds, we incorporate RFCA-Conv, which enhances feature extraction by aggregating information from different receptive fields. This convolution, combined with CA attention mechanisms, allows the model to effectively capture features from various positions, overcoming the limitations of standard convolutions.