Building cracks are significant indicators of structural integrity. Conventional fracture detection methodologies, however, are characterized by extended durations, significant labor requirements, and limitations in both precision and operational effectiveness. Findings are also subject to subjective and technical constraints inherent in manual assessments. To overcome these challenges, this paper introduces an enhanced YOLOv8-based methodology for developing a building crack detection system, thereby achieving high precision, operational efficiency, and cost-effectiveness. Initially, classified and segmented datasets of building fractures were obtained from field photography, online image aggregation, and open-source databases, thereby providing the basis for training the experimental model. Subsequently, the Swin Transformer window multi-head self-attention mechanism was implemented to augment small-object recognition capabilities and reduce computational demands, thereby enabling the development of an enhanced image segmentation module. Utilizing the U-Net’s segmentation capabilities, a rotated split method was implemented to quantify fracture width and derive geometric parameters from the segmented crack regions. In order to evaluate the effectiveness of the model, two experiments were conducted: one to demonstrate the performance of the classification category and the other to show the capabilities of the segmentation category. The result is that the proposed model has high accuracy and efficiency in the frac detection task. This approach effectively enhances fracture detection in structural safety evaluations of these buildings, providing technical support for relevant management decisions.
Zuo et al. (Thu,) studied this question.