Pavement Crack Detection Method Based on Composite Image Double CNN Network
To establish the pavement crack detection model based on deep learning convolutional neural network(CNN)and to improve the identification accuracy of special pavement cracks(e.g.,white crack,shallow crack,wet crack,and repair crack),based on the single CNN structure(single network),the pavement crack detection method based on composite image double CNN network was proposed.First,based on the input gray image,the corresponding binary image was added to form the composite image channel considering the crack disease image characteristics.Second,on the basis of single network structure of CNN,the single network for special crack detection was added,so as to form the composite image double network structure.The non-special crack network training used all data;the special crack network training used special crack data;and the two network parameters were updated independently;thus the composite image double CNN network was formed.Then,two networks judged the same test data respectively to obtain their own probability matrix.Finally,according to the principle of probabilistic one-sided suppression,the output results of two single networks were superposed to get the final recognition results.There were 700 000 images collected by detection vehicles for training and detecting the composite image double network method.The result indicates that the intersection over union,precision and recall of the composite image double network are significantly better than those of gray image single network.Two proposed optimizations(i.e.,transforming the single-channel grayscale map into a two-channel composite input map,and adding a special crack identification network)are proved to improve the detection ability for non-special crack and special crack areas.In addition,the intersection over union,precision and recall of the composite image double network are higher than those of other deep learning pavement crack detection algorithms.
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