Building Damage Detection for Extreme Earthquake Disaster Area Location from Post-Event Uav Images Using Improved SSD
- Resource Type
- Conference
- Authors
- Li, Xiaoli; Yang, Jiansi; Li, Zhiqiang; Yang, Fan; Chen, Yahui; Ren, Jing; Duan, Yihao
- Source
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International. :2674-2677 Jul, 2022
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Training
Deep learning
Computer vision
Computational modeling
Earthquakes
Buildings
Geoscience and remote sensing
building damage detection
UAV
modified SSD
- Language
- ISSN
- 2153-7003
It is of vital importance to locate the extreme earthquake disaster area timely and immediately after the earthquake occurs, which is greatly helpful for first responders to rescue trapped victims. The development of deep learning, computer vision technology as well as the widespread availability of inexpensive unmanned aerial vehicles (UAVs) has brought a new opportunity for building damage detection. We aim to propose a SSD model integrated with Convolutional Block Attention Mechanism (SSD_CBAM) and use it to detect damaged buildings caused by an earthquake. On the basis of the building damage dataset derived from the UAV images in Wenchuan Ms8.0 Earthquake, we compared our model with the classical SSD model. The result shows that detection accuracy is increased by 3.72% after the attention mechanism is integrated. It is believed that the result will be even better in the case of a lager training dataset.