Ancient Building Crack Detection Based on YOLOv8 Algorithm
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Abstract
This study investigates the use of the YOLOv8 algorithm for detecting cracks in ancient buildings. Utilizing a dataset from Kaggle, the model was trained to identify crack patterns with high accuracy. The YOLOv8 model achieved a precision of 1.00 at a confidence level of 0.823 and an overall accuracy of approximately 92%. These results demonstrate the effectiveness of YOLOv8 in accurately detecting and monitoring structural cracks, making it a valuable tool for the preservation of culturally significant structures. Future research will aim to enhance the model's capabilities and explore its integration with automated inspection technologies.
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