Abstract: With the development of transportation, bridges play an increasingly important role in the transportation process ，and bridges are more diversified. Therefore, in the face of a large number of bridges with different working conditions, it is particularly important to develop an intelligent crack detection technology that can conveniently learn new working conditions. To improve the accuracy and efficiency of the target detection algorithm, this paper divides the original crack image into slices with three sizes of resolution, and trains the network to recognize cracks of different sizes. To increase the subsequent expansion of the algorithm at the same time, we design a method to adaptively adjust the anchor box according to the training set dimension, so the algorithm can directly add the data for training and automatically adjust the optimal anchor box size when need to increase the training data for different engineering conditions in subsequent process, which makes the algorithm useful for actually applying. Compared with the original YOLOv3 network and some algorithms in references, the accuracy of the proposed algorithm is 91% on average and the scalability is better.

Key words: machine vision, crack detection, target detection, CNN