Earthquake damaged buildings identification based on improved YOLOv8

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.