A CNN-based network with attention mechanism for autonomous crack identification on building facade

This paper presents a rapid and precise deep learning-based approach for measuring cracks in concrete structures. The proposed methodology involves data acquisition, pre-processing, model construction and post-processing. The Deeplabv3+ model is used for crack detection, and the use of Coordinate Attention is introduced to enhance its performance. And the implications of attention locations in the model are discussed. Insertion position after atrous spatial pyramid pooling (ASPP) operation is the most effective and accurate. The crack widths are obtained through post-processing, and the actual width is determined using the width conversion equation. The proposed method achieves an effective crack detection MIoU of 85.95 and a calculated width error of 6.3%, with a reduction of 2.5% compared to traditional models. Numerical experiments and real-world building experiments have demonstrated the feasibility and effectiveness of the proposed method. Overall, the proposed method presents a fast and dependable crack detection technique that has great potential for practical application.