Title: ROADSIGHT: A novel dataset for real-time intersection detection in aerial scenes under seasonal variation
Primary area: datasets and benchmarks
Keywords: Road intersection detection, aerial imagery, UAV dataset, YOLOv11, edge computing, seasonal variation, real-time detection, deep learning

Abstract: Road intersections serve as critical nodes in transportation networks, and their accurate detection from aerial imagery is essential for applications such as autonomous navigation, urban planning, and traffic management. However, existing datasets for object detection in aerial views often lack specificity to intersections, diversity in seasonal conditions, and customized annotations for unmanned aerial vehicle (UAV) captured data, which leads to challenges in model robustness and real-time performance on edge devices. Motivated by these gaps, this work introduces a novel dataset named ROADSIGHT comprising high-resolution UAV images of road intersections collected under varying environmental conditions, including winter and summer, with precise annotations for categories such as, roundabouts, 3-leg (T/Y) and 4-leg intersections. The dataset creation process is inherently difficult to replicate due to the requirement for specialized UAV equipment, weather-dependent data collection windows, manual expert annotation of complex intersection geometries, and access to diverse geographic locations with varying road infrastructure. We evaluated the dataset using state-of-the-art YOLOv11 models, achieving high mean Average Precision (mAP) while optimizing to reduce inference time for deployment on resource-constrained UAV platforms. This approach not only addresses the limitations of prior datasets but also enables efficient real-time intersection mapping, outperforming benchmarks on similar aerial tasks by enhancing environmental adaptability and detection speed.