Post-earthquake bridge crack detection is a critical step in assessing structural safety. Traditional manual detection of bridge cracks is time-consuming, labor-intensive, and poses significant risks. This paper focuses on the automatic identification of structural cracks by analyzing their morphology, orientation, and distribution characteristics, and preliminarily distinguishes them from non-structural damages such as surface stains and coating peeling. Therefore, this paper proposes a bridge crack recognition algorithm based on image processing. First, the input crack image undergoes preprocessing to obtain a binary image, reducing measurement errors caused by environmental factors or uneven illumination, using an improved Retinex algorithm to enhance image brightness. Second, an improved wavelet transform method is employed to remove large-area noise. Then, connected component analysis is used to filter out point-like and patch-like noise, resulting in a complete and clear crack skeleton. Finally, the crack length, width, and other characteristic values are obtained using an image pixel coordinate calculation method, achieving non-contact, non-destructive measurement of concrete surface crack characteristics. The algorithm is based on two-dimensional image processing and does not directly measure crack depth, but the extracted parameters such as length, width, and area ratio provide important surface-based evidence for rapid post-earthquake bridge structural safety assessment. Multiple experimental results show that the proposed algorithm has a maximum width measurement relative error of less than 2.3%, a length measurement relative error within 8%, and an average peak signal-to-noise ratio (PSNR) of the denoised image increased to 74.73 dB. This algorithm provides an effective automated detection tool for rapid post-earthquake bridge safety assessment.
Yang et al. (Mon,) studied this question.