# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 4 Discussion  
This paper proposes an insulator defect detection method for UAV aerial images, which enhances multi-scale target detection capabilities while reducing the model's parameter count. The proposed algorithm introduces a multi-scale feature extraction network (MSFENet) that addresses the limitations of conventional FPNs, including insufficient information fusion and inadequate focus on large-scale feature layers. To overcome these issues, a Two-Stage Feature Fusion (TSFF) module is introduced, enabling the fusion of features from different levels and enhancing the model's multi-scale detection performance through a multi-head detection strategy. To address the problem of excessive parameters in existing object detection algorithms, a lightweight Multi-Scale Shared Parameter Head (LMSSPH) is proposed. This detection head adopts a shared parameter design, effectively reducing the model's size and parameter count while maintaining robust performance by leveraging a multi-head detection strategy. Considering the challenge of insufficient feature extraction in complex backgrounds, which often leads to degraded insulator detection performance, this study incorporates RFAConv--a convolutional module that aggregates features with varying receptive fields. This design enables the model to effectively capture features from different spatial locations.  
Experimental results on the UID and SFID datasets demonstrate that RML-YOLO achieves high detection accuracy for insulators and defects, with AP50 values of 88.3% and 99.3%, respectively, indicating that the model can effectively detect most insulator targets. Furthermore, experiments on the VISDrone2019 dataset confirm the algorithm's generalization capability, achieving an AP that is 2.1% higher than that of Drone-YOLO, a dedicated target detection algorithm for UAV aerial scenarios. This result demonstrates that the model can accurately detect not only insulator targets but also other objects in UAV-captured scenes.  
The YOLO series of algorithms has undergone continuous evolution and iteration. This study aims to develop improvements that can serve as plug-and-play modules, making them adaptable to other object detection algorithms. This article conducted an improvement experiment on YOLOv11 on the UID dataset, and the results are presented in Table 7. The findings reveal that the improved algorithm achieves accuracy comparable to that of YOLOv8. However, despite the lightweight design of the detection head, the overall parameter count of RML-YOLOv11 is higher than that of YOLOv11 due to the integration of MSFENet and RFAConv. This suggests that local model lightweighting may not be universally applicable to all algorithms. Future articles will conduct experiments and promote more methods.  
Although RML-YOLO has demonstrated promising performance in insulator defect detection, certain limitations remain. Specifically, while the use of a shared parameter detection head effectively reduces the model's parameter count, optimizing the detection head alone is insufficient for significantly reducing the overall model size. Therefore, future research could further optimize the model through the following directions: First, other lightweight technologies such as model pruning, quantization, and distillation could be considered to further compress model dimensions and enhance performance. Second, given that the purpose of lightweight design is to enable deployment on resource-constrained devices, future plans include directly deploying the optimized model on unmanned aerial vehicles (UAVs) equipped with edge computing devices after completing lightweight modifications. This implementation would achieve real-time detection while improving system response speed and stability in practical field applications.