YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery

This paper presents a robust approach for object detection in aerial imagery using the YOLOv5 model. We focus on identifying critical objects such as ambulances, car crashes, police vehicles, tow trucks, fire engines, overturned cars, and vehicles on fire. By leveraging a custom dataset, we outline the complete pipeline from data collection and annotation to model training and evaluation. Our results demonstrate that YOLOv5 achieves mAP@0.5 of 81.7% and mAP@0.5:0.95 of 62.7%, achieving an optimal balance between speed (30 FPS) and accuracy. This work addresses key challenges in aerial imagery, including small object detection and complex backgrounds, and provides insights for future research in automated emergency response systems.