CrackUnet: a novel network with joint network-in-network structure and deformable convolution for pavement crack detection

Automatic pavement crack detection is a critical technique in the intelligent transportation system, which can effectively replace the person with a machine to detect the pavement crack automatically. This task is excellently challenging due to the tiny texture and the various shapes of each crack object. Previous crack detection networks mainly aim to perform complicated multi-scale feature fusion to learn the semantic information of cracks. However, the typical symmetrical networks with high-to-low resolution are undesirable to extract detailed crack texture information, and these existing methods cannot effectively deal with the issue of the various shapes.This paper proposes a novel end-to-end U-shaped convolutional neural network, termed CrackUNet, for the pavement crack detection task. To extract the information of the tiny texture, we design a novel network-in-network structure, which can enlarge the receptive field and obtain multi-scale features by putting a sub-network into each convolutional layer. Besides, to handle the issue of the various shapes for the crack object, we exploit the deformable convolution to capture contextual information of each crack, which can further improve the performance of crack detection. We train and evaluate the proposed CrackUNet on three public pavement crack datasets. The quantitative experimental results illustrate that our network outperforms the current state-of-the-art methods with almost the same efficiency. Specifically, the precision, recall, and F1-score of the CrackUNet are approximately 92.27%, 93.99%, and 92.94%, respectively.