Foreign object detection algorithm for transmission lines based on improved RT-DETR
Fudi Ge, Yunfei Ding, Xingtao Wu, Yuxin Si, Lina Wang, Dong Ding, Xichao Wang, Hongwei Zhang · Engineering Research Express · 2025
Abstract In order to solve the problems of complex background, variable target scale, and frequent false and missed detections in transmission line foreign object detection, an algorithm based on improved RT-DETR is proposed in this paper. The algorithm enhances the feature extraction capability and background interference suppression by introducing a CRMB module with integrated inverted residual shift module (iRMB) and cascade group attention (CGA). In addition, a SSFF-Slimneck cross-scale feature fusion network is proposed to mitigate the information loss during feature fusion. Focaler-Shape-IoU is adopted as the bounding box loss function to accelerate model convergence, enhance generalisation capability and improve detection performance. The experimental results show that the proposed method improves 3.3% and 2.3% on mAP@50 and mAP@50:95, respectively, while the parameters and computation are reduced by 24.5% and 16.4%, respectively. This indicates that the proposed method achieves higher detection accuracy while reducing the computational complexity, which significantly improves the foreign object detection capability of transmission lines.