Real-Time Damaged Building Region Detection Based on Improved YOLOv5s and Embedded System From UAV Images

Detecting the damaged building regions is vital to humanitarian assistance and disaster recovery after a disaster. Deep-learning techniques based on aerial and unmanned aerial vehicle (UAV) images have been extensively applied to the literature to detect damaged building regions, which are approved to be effective methods for fast response actions and rescue work. However, most of the existing building damaged region detection methods only consider the extraction accuracy of damaged regions from aerial or UAV images, which are not real time and can hardly meet the practical application of emergency response. To address this problem, a new real-time building damaged region detection based on improved YOLOv5 and adapted to an embedded system from UAV images is proposed, which is named DB-YOLOv5. First, residual dilated convolution module is employed to extract the spatial features, which can increase the receptive field. Then, a feature fusion module (BDSCAM) is designed to enhance the expressive ability of object feature, which could improve the classification performance of detector. Finally, a double-head method, an integration system of fully connected and convolution head for bounding box regression and classification, executes the localization task. The proposed DB-YOLOv5 method was evaluated using postdisaster UAV images collected over Ludian, China, in 2013 and Beichuan, China, in 2008. We found that the experimental results demonstrate that the proposed method is high accuracy and efficient for building damaged region detection and assessment on the embedded system. This approach is robust and suitable for practical application in disaster scenarios.