Detection and assessment of post-earthquake functional building ceiling damage based on improved YOLOv8

Ceiling is one of the crucial non-structural components in public buildings, but it is susceptible to damage after earthquakes. For important functional buildings, rapid assessment of their safety, economic losses, and recoverability is crucial post-earthquake. However, the current detection and assessment of ceiling damage in earthquake-stricken areas heavily relies on manual efforts. Due to limitations in detection methods and technological levels, these manual efforts struggle to meet the requirements of speed, accuracy, and safety. In response to these challenges, this study proposes a post-earthquake functional building ceiling damage detection and assessment method based on GTS-YOLOv8. Firstly, a dataset containing 1059 real ceiling damage images is established, categorized into two damage types: “Panels Fallen" (PF) and “Complete Destruction" (CD). Next, Ghost convolution is introduced, combined with Swin Transformer blocks, and a novel Slim Neck structure is designed to enhance the YOLOv8 algorithm. The GTS-YOLOv8 object detection model is presented. On the established dataset, GTS-YOLOv8 achieves 92 % mAP50 and 72.2 % mAP50-95, surpassing the baseline YOLOv8 by 2.3 % and 4.1 %, respectively. The study investigates ceiling damage detection under different lighting conditions and shooting distances. The results indicate that the highest detection accuracy is achieved under well-lit conditions and when the shooting perspective covers the entire ceiling area. Finally, based on the research outcomes, a detection and assessment architecture is constructed, and a corresponding system is developed, capable of automatically determining the damage State based on ceiling damage detection results. This architecture provides a foundation for the rapid assessment of building safety, economic losses, and recoverability post-earthquake.