Optimizing Hazard Detection with UAV-UGV Cooperation: A Comparative Study of YOLOv9 and Faster R-CNN

This paper presents a collaborative hazard-detection system that pairs a UAV running YOLOv9 for rapid aerial scanning with a UGV running Faster R-CNN for precise ground-level confirmation. The pipeline exploits complementary strengths, fast wide-area cueing from the air and high-precision verification on the ground, to reduce false alarms while maintaining responsiveness in complex environments. On the validation set, YOLOv9 reached mAP@0.5 = 0.969 with F1 = 0.95 at 41.7 FPS, enabling real-time scanning of large areas. Faster R-CNN attained mAP@0.5 = 0.979 with F1 = 0.95 at 1.72 FPS, providing reliable close-range confirmations where localization accuracy is critical. Together, these results show that the proposed UAV–UGV pipeline delivers a practical balance between rapid hazard identification and trustworthy validation, suitable for search and rescue, critical infrastructure monitoring, and operations in hazardous environments. Potential extensions include inference optimization on the ground platform, multi-sensor data fusion, and field trials to assess robustness under real-world conditions.