Earthquake damaged buildings identification based on improved YOLOv8

Zilin Ding; Xinqiang Yao; Yajing Li

doi:10.1117/12.3049990

8 November 2024 Earthquake damaged buildings identification based on improved YOLOv8

Zilin Ding, Xinqiang Yao, Yajing Li

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163P (2024) https://doi.org/10.1117/12.3049990
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

After a destructive earthquake, rapid evaluation of damaged buildings information is crucial for effective disaster relief efforts. Traditional data collecting techniques are often slow and insufficient to meet the urgent demands of earthquake response. To address this issue, this study introduces an improved algorithm derived from the you only look once version 8 (yolov8) model, tailored for the identification of damaged building components post-earthquake. In this study, the information extraction section of the backbone of YOLOv8 is improved. The Parallel Attention Mechanism Model (PAM) is introduced to improve the model's ability to deal with complex scenarios. Apart from that, the SimSPPF structure is introduced to optimize the feature pyramid layer, which can increase the speed. The results show the effectiveness of the improved YOLOv8 algorithm in identifying the damaged constructs of earthquake-damaged buildings. Average accuracy improved by 3.2% compared to the original model. The method can provide a valuable reference for the development of automatic analysis methods for earthquake information.

Citation Download Citation

Zilin Ding, Xinqiang Yao, and Yajing Li "Earthquake damaged buildings identification based on improved YOLOv8", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163P (8 November 2024); https://doi.org/10.1117/12.3049990

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