Balancing Accuracy and Efficiency for Road Intersection Detection Method in Large-Scale High-Resolution Remote Sensing Imagery

Road intersections serve as critical topological nodes in transportation networks, and their rapid, accurate identification is essential for intelligent transportation systems and geographic information updates. However, direct detection of road intersection on large-scale high-resolution remote sensing imagery remains challenging due to intersection structural diversity, complex illumination conditions, and vegetation occlusion, which make it difficult to balance detection accuracy and computational efficiency. To address these challenges, SW-GSE- YOLO, a novel lightweight and high-precision road intersection detection model, is proposed. The model incorporates three key innovations: (1) a Swin-C2PSA module that is designed by integrating the shifted-window self-attention mechanism of the Swin Transformer with the C2PSA structure, enabling efficient large-scale feature modeling while enhancing local detail and global contextual representation; (2) a GS3k2 lightweight network architecture is built upon GSConv and CSPNet, which reduces the parameter count while preserving feature extraction capability; and (3) the Efficient Intersection over Union (EIoU) loss function is adopted to optimize bounding box regression and improve intersection center localization accuracy. To validate the proposed method, the RIS-42k dataset is constructed, comprising 42,372 annotated image patches of size 400×400 pixels. Experimental results demonstrate that SW-GSE-YOLO outperforms recent mainstream lightweight detectors, including YOLOv8–YOLOv12 and RT-DETR. Compared with the YOLO11n baseline, it achieves improvements of 5.9%, 1.3%, 3.9%, and 14.6% in Precision, Recall, mAP@0.5, and mAP@[0.5:0.95], respectively, while significantly reducing GPU memory usage and inference latency for large-scale image processing. Furthermore, SW-GSE-YOLO demonstrates robust localization accuracy, and strong generalization capability across complex urban, suburban, and rural environments.