Pavement crack detection from images is a challenging problem due to intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. Traditional learning-based approaches have difficulties in obtaining representative training samples. We propose a new unsupervised multi-scale fusion crack detection (MFCD) algorithm that does not require training data. First, we develop a windowed minimal intensity path-based method to extract the candidate cracks in the image at each scale. Second, we find the crack correspondences across different scales. Finally, we develop a crack evaluation model based on a multivariate statistical hypothesis test. Our approach successfully combines strengths from both the large-scale detection (robust but poor in localization) and the small-scale detection (detail-preserving but sensitive to clutter). We analyze and experimentally test the computational complexity of our MFCD algorithm. We have implemented the algorithm and have it extensively tested on three public data sets, including two public pavement data sets and an airport runway data set. Compared with six existing methods, experimental results show that our method outperforms all counterparts. Specifically, it increases the precision, recall, and F1-measure over the state-of-the-art by 22%, 12%, and 19%, respectively, on one public data set.
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