Abstract: Surface crack identification with visual images is a kind of non-contact detection solution, which is not limited by the material of the tested object and is easy to achieve online automation. Therefore, it has the advantages of fast speed, low cost and high precision. Firstly, the public data sets of typical pavement crack are comprehensively collected, and the characteristics of sample images and the random variable factors are summarized. Subsequently, the advantages and disadvantages of hand-crafted feature engineering, machine learning, and deep learning crack identification methods are compared. Finally, from the aspects of network architecture, testing performance and predicting effectiveness, this paper reviews the development and progress of typical deep learning algorithms such as self-built CNN, transfer learning (TL) and encoder-decoder (ED) that can be easily trained and deployed. We can see the obvious improvement of performance and effect because of algorithm optimization and computing power enhancement. The results show that real-time patch-level and fast pixel-level crack detection can be realized on low computing power GPU platform.

Key words: pavement crack detection, visual image identification, deep learning