Abstract
Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable.
Highlights
China is one of the countries with the highest earthquake losses in the world
YOLOv5s-ViT-Bi-Directional Feature Pyramid Network (BiFPN) first remains stable starting from the vertical orange line, and its average precision (AP)
Regarding the complex background for damaged houses as well as the inconsistent resolution of Unmanned Aerial Vehicle (UAV) images, we employed the latest You Only Look Once (YOLO) model, named YOLOv5, and optimized its network architecture for the Backbone and Neck modules to improve the model in view of the demand of high accuracy and strong timeliness and proposed an improved model, YOLOv5s-ViT-BiFPN
Summary
China is one of the countries with the highest earthquake losses in the world. More than 50% of the total cities and 70% of the great and middle cities in China are located within areas with more than VII degree of earthquake intensity [1] and are threatened by moderate and massive earthquakes. China has experienced many devastating earthquakes with heavy losses. The 12 May 2008 Wenchuan Ms8.0 earthquake in Sichuan Province, with an epicentral intensity of degree XI, led to more than 440,000 square kilometers of affected area, and more than 15 million rooms collapsed [2]. Yushu Ms7.1 earthquake in Qinghai Province caused more than 80% of the buildings in. According to China Earthquake Administration (CEA), Remote Sens.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
