Multi-level convolutional neural network for asphalt pavement crack image hierarchical filtering

The quick and accurate identification and evaluation of asphalt pavement crack disease has become one of the important tasks of pavement maintenance and road safety. There are a number of non-crack images in the actual collected pavement images. On the premise of ensuring that there is no missing filter in the crack image, it is of great practical significance to improve the precision of crack images and true negative rate of non-crack pavement images as high as possible, thus reducing the work intensity of manual filtering, as well as subsequent automatic crack segmentation and disease damage assessment. A multi-level convolutional neural network method for asphalt pavement image filtering was proposed, which consists of three stages, i.e, training, fine-tuning and validation. The input fine-tuning increment of softmax layer was obtained using fine-tuning set. In order to avoid the problem that the precision decreases when the recall of crack image increases, based on the comparison of the similarities and differences of non-crack images excluded by different convolutional neural networks, a hierarchical processing model was proposed, in which the improved AlexNet was employed as the first level filtering network and VGG16 or ResNet50 as the second or third level filtering network. The experimental results on noisy and complex road images show that the three-level hierarchical filtering model can achieve high true negative rate and high accuracy when recalling crack images 100%. Compared with other methods, the experimental results show that the proposed method can effectively solve the problem of missing filter in asphalt pavement crack image, and can produce a better detection effect.