YOLOv5-Based Object Detection for Emergency Response in Aerial Imagery
This work addresses the problem of automated emergency response for aerial imagery users, but it is incremental as it applies an existing method to a new domain.
The paper tackled object detection in aerial imagery for emergency response by applying YOLOv5 to identify critical objects like ambulances and car crashes, achieving a balance of speed and accuracy suitable for real-time applications.
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 effectively balances speed and accuracy, making it suitable for real-time emergency response applications. 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.