Disaster Damage Detection using YOLOv5

Abstract

Abstract: Disasters, whether natural or caused by human actions, can have severe consequences for communities and infrastructure. Swift and accurate damage assessment is essential for effective response and recovery efforts. However, traditional assessment methods are often slow, labor-intensive, and prone to errors.To address this challenge, the proposed approach utilizes the YOLOv5 object detection model. YOLOv5 is renowned for its speed and accuracy, making it suitable for real-time applications where timely assessments are critical. The methodology focuses on detecting various types of damage, such as structural damage, debris, and flooding, in disaster-affected areas.The model is trained on an annotated image dataset that includes examples of different damage types and extents. By automating the damage detection process, emergency responders can prioritize intervention areas and allocate resources more efficiently. Overall, this approach has the potential to significantly enhance the speed and accuracy of disaster damage assessment, ultimately improving response and recovery efforts.