Table 2 Key tasks and application scenarios of the relevant reference
From: Deep learning-driven pathology detection and analysis in historic masonry buildings of Suzhou
Reference | Key Task | Model | Publication Year | Title |
|---|---|---|---|---|
Object Detection | Yolov5 | 2022 | Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm | |
Object Detection | Yolov4 | 2024 | Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: a case study of the classical gardens of Suzhou | |
Object Detection | Yolov5 | 2023 | Detection of limestone spalling in 3D survey images using deep learning. Automation in Construction. | |
Object Detection | Yolov4 | 2023 | Recognition of damaged types of Chinese gray-brick ancient buildings based on machine learning—taking the Macau World Heritage Buffer Zone as an example | |
Object Detection | Yolov4 | 2023 | Non-destructive testing research on the surface damage faced by the Shanhaiguan Great Wall based on machine learning | |
Object Detection | Yolov7 | 2024 | Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings | |
Image Segmentation | Yolov8 | 2024 | Intelligent assessment system of material deterioration in masonry tower based on improved image segmentation model. |
