Pavement crack detection algorithm using fused attention mechanism and densely connected feature

Abstract Precise and intelligent crack detection is essential for prolonging the service life and ensuring cost-effective maintenance of pavement. This study develops a crack detection system inspired by computer vision, utilizing the attention mechanism and dense connectivity features. The coding component consists of ResNet-50 integrated with an attention mechanism, which improves feature extraction capabilities and rapidly identifies crack locations. During decoding, the characteristics of the encoding part and its low-level information are taken into account, enhancing segmentation accuracy. In comparison to other common approaches, it demonstrates superior accuracy in F1-Score, IoU, PA, and other evaluation metrics on the cracks dataset captured by the authors and CFD. The segmentation facilitates the automatic extraction of crack length, area, and maximum width by the linear fitting or point-by-point curve-fitting technique, grid method, and maximum inscribed circle approach, respectively. The results align well with the measured values, and the computational viability of the pavement condition index is analyzed, demonstrating its practicality in the intelligent assessment of pavement health.