  Compared to NDT and health monitoring method for cracks in engineering
structures, surface crack detection or identification based on visible light
images is non-contact, with the advantages of fast speed, low cost and high
precision. Firstly, typical pavement (concrete also) crack public data sets
were collected, and the characteristics of sample images as well as the random
variable factors, including environmental, noise and interference etc., were
summarized. Subsequently, the advantages and disadvantages of three main crack
identification methods (i.e., hand-crafted feature engineering, machine
learning, deep learning) were compared. Finally, from the aspects of model
architecture, testing performance and predicting effectiveness, the development
and progress of typical deep learning models, including self-built CNN,
transfer learning(TL) and encoder-decoder(ED), which can be easily deployed on
embedded platform, were reviewed. The benchmark test shows that: 1) It has been
able to realize real-time pixel-level crack identification on embedded
platform: the entire crack detection average time cost of an image sample is
less than 100ms, either using the ED method (i.e., FPCNet) or the TL method
based on InceptionV3. It can be reduced to less than 10ms with TL method based
on MobileNet (a lightweight backbone base network). 2) In terms of accuracy, it
can reach over 99.8% on CCIC which is easily identified by human eyes. On
SDNET2018, some samples of which are difficult to be identified, FPCNet can
reach 97.5%, while TL method is close to 96.1%.
  To the best of our knowledge, this paper for the first time comprehensively
summarizes the pavement crack public data sets, and the performance and
effectiveness of surface crack detection and identification deep learning
methods for embedded platform, are reviewed and evaluated.
