Research on Crack Segmentation and Detection of Red Brick Wall Structure based on Deep Learning

The present paper discusses a technique for crack segmentation and detection in red brick walls that is based on deep learning. This technology is designed to improve the efficiency and accuracy of assessing building safety. With the development of the construction industry, the detection of cracks in red brick walls has become particularly important. Traditional detection methods are labor-intensive and error-prone, while deep learning models provide an efficient and reliable solution. In this paper, we study a variety of deep learning models, including PSPNet, DeepLabV3+, ERFNet, ANN, CCNet, and SegFormer, and compare their performance in the wall crack detection and segmentation task through experiments that use a real scene dataset to validate the model’s accuracy and generalization ability in the presence of interfering factors. Experimental results show that the SegFormer model performs best in IoU, F1, ACC and Recall, reaching 65.99%, 77.37%, 99.87%, and 80.79%, respectively, and with the addition of the attention mechanism to the SegFormer model for optimization, the model’s IoU and F1 are improved by 1.16% and 1.13%, respectively. The performance was significantly improved. The results provide technical support for detecting and repairing cracks in red brick walls, which helps to detect and repair potential safety hazards in a timely manner.