# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 5 Conclusions  
In this paper, an object detection algorithm named RML-YOLO is proposed for the problems of varied and uneven object scales, large volume and parameter number of conventional detection models, and difficult object feature extraction in complex environments, and experiments on the homemade dataset UID and the public dataset SFID, VISDrone2019, can be concluded. * Diverse object scales and feature extraction in the context of complex environments are the main reasons affecting the circuit line insulator and defect detection. This paper proposes an MSFENet, which is different from the traditional feature fusion network in that it not only fuses two large-scale feature maps of B1 and B2 using the TSWF module proposed in this paper, but also inputs the feature layers of four different scales of P2, P3, P4, and P5 to the detection head for multi-head detection, which substantially improves the multi-scale detection capability of the model; for the problem of large number of algorithm parameters, the article proposes an LMSH, which uses the idea of shared feature design. This design not only reduces the number of model parameters and computation, but also solves the problem of uneven distribution of objects at different scales, further improving the model's ability of object detection at different scales, and lastly, the article introduces RFCAConv, which summarizes the different sensory field features, to solve the problem caused by the use of a shared-parameter convolutional kernel to solve the problem of insufficient standard convolutional feature extraction due to the use of a shared parameter convolution kernel. * The comparative experimental results show that the number of parameters of the algorithm proposed in this paper has decreased by 500,000, and the model volume has decreased by 0.8 M, which facilitates the subsequent deployment of the model on UAVs, and at the same time, the algorithm improves the AP by 7.8% on the UID dataset and by 2.74% on the SFID dataset, and improves the AP on the homemade insulator dataset and the public insulator dataset, and compares with the other classical algorithms and the public insulator dataset. Improvement, and compared with other classical algorithms and state-of-the-art algorithms, still maintains the leading level, which proves the detection ability of the algorithm proposed in this paper in insulators and defect scenarios. On the VISDrone2019 dataset, AP improves by 3.9%, demonstrating the generalization ability of the algorithm across different datasets and environments. In conclusion, the RML-YOLO algorithm successfully combines lightweight design with strong detection performance and generalization ability. It effectively handles the detection of insulators and defective objects in complex backgrounds and across multiple scales, making it highly suitable for real-world applications, especially in UAV-based inspection scenarios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **AP** & **AP50** & **AP75** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **Param (Million)** & **Size (MB)** \\ \hline YOLOv8 & 57.88 & 82.81 & 65.53 & 51.3 & 63.6 & 56.9 & 3.0 & 6.3 \\ YOLOv11 & 58.3 & 83.1 & 65.8 & 51.9 & 63.4 & 60.0 & 2.58 & **5.5** \\ RML-YOLOv8 & **65.7** & 88.3 & **76.6** & **61.3** & **66.3** & 63.6 & **2.51** & **5.5** \\ RML-YOLOv11 & **65.7** & **88.4** & 76.2 & 61.1 & 66.2 & **64.6** & 2.79 & 5.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: YOLOv11 improvement experiment. **Author Contributions:** Conceptualization, Z.D. and X.L. ; methodology, Z.D. and X.L. ; software, X.L. ; validation, X.L. ; formal analysis, R.Y. ; writing--original draft preparation, Z.D. and X.L. ; writing--review and editing, Z.D., X.L. and R.Y. ; visualization, X.L. ; project administration, Z.D., funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the Guangxi Science and Technology Project (Nos.