How to extract and collect crack information efficiently and effectively still remains a challenging task due to illumination

lane and stains over the pavement images. In this paper

based on the sub-patch discriminant analysis

we propose a novel pavement crack detection method to address the foregoing problem. First

an intensity compensation based grayscale correction algorithm is presented to weaken uneven illumination

then the sparse autoencoder model is applied to extract sub-patch features. Second

in order to extract more discriminative features

a new two class iterative discriminant analysis is further proposed

where the projection and clustering processing steps are alternatively performed to update the inter-distance of different sub-classes of all crack patches until convergence. Finally

the nearest neighbor classifier is adopted in the discriminative subspace for classification tasks. As the distribution of samples in the transformed subspace approaches to the true one via the iterative process

discrimination of features can be enhanced significantly. A series of experiments show that the proposed method achieves high recognition rates

i.e.

up to 95.5% on the benchmark dataset

90.9% on a practical highway dataset. A sub-patch discriminant analysis based method is developed for effective crack detection. Our method aims to extract highly discriminative features for sub-patches of road images. Three main steps

i.e.

grayscale correction

sparse autoencoding

iterative discriminant feature extraction are involved

making our method highly robust and adaptive to the road images with several kinds of heavy noises. The final classification is performed in the obtained low dimensional subspace. Extensive experimental results on two datasets demonstrate that our proposed method generally outperforms other existing related algorithms.