Crack Detection Algorithm Based on Image Analysis

This paper proposes an improved crack detection algorithm for fair-faced concrete based on image analysis, by introducing a new deep learning model: Enhanced Crack Detection Network (ECDNet), achieving significant breakthroughs over traditional crack detection models. Compared with traditional edge detection algorithms, ECDNet integrates multi-scale convolution features and self-attention mechanisms, enabling more accurate identification of cracks with different widths and orientations. We also optimized the data preprocessing steps, employing a novel contrast enhancement technique to effectively improve the detectability of cracks in low-quality images. Experimental results show that compared with classic crack detection models such as Canny operators and Sobel filters, ECDNet improves detection accuracy while reducing false alarm rates. This research not only provides an efficient tool for safety assessment of fair-faced concrete structures but also offers a new perspective for the application of computer vision in civil engineering.