Road intersection detection based on YOLOv5s and topological analysis: a case study of Shijiazhuang

Abstract

As urbanization and the rapid development of remote sensing technology, the interpretation of urban roads and intersections using high-resolution remote sensing images has become the focus of studying urban planning and road network structure. However, the current research is only limited to extracting road intersections, and few studies have constructed their topological relationships and analyzed the spatial distribution of road networks. Therefore, in this study, the intersection targets are extracted based on YOLOv5s model, and then the topological relationship of intersections is constructed by Delaunay Triangulation. The accuracy of the optimal model is 0.9415 for mAP, 0.8985 for F1, 0.8611 for Recall, and 0.9394 for Precision. By analyzing their characteristics such as area, side length, PESL, AESL, ESLR and interior angle, it can be found that the density of road intersections in the study area is high, the spacing of adjacent intersections of most roads is relatively similar, and most of the roads intersect vertically. This study first extracts road intersections from remote sensing images and then analyzes their topological relationships to provide research ideas for large-scale urban road network analysis and multi-source road data matching.