Abstract: In concrete road pavements,the presence of cracks often leads to major engineering and economic problems.At present,when computer vision technology is used to conduct crack detection,artificial predesigned feature extractor is needed to extract image features for classification,resulting in poor generalization ability and classification perfor-mance.In this paper,a crack detection method based on convolutional neural network was proposed to realize the automatic detection and classification of pavement defects and improve the efficiency and accuracy of pavement crack detection.Firstly,the crack convolutional neural network of concrete pavement is designed.The model is based on AlexNet network architecture,and the model is optimized from two aspects:network structure level and hyperparameter.Secondly,the camera collects the concrete pavement image to obtain the learning data.According to the data set size and the image color factor,10000 and 20000 gray maps and four data sets of the color RGB map are respectively created.Then,the created four datasets are used.The data set trains the designed concrete crack convolutional neural network to create a crack detection model and compare it to the original AlexNet model.Finally,the two models are compared by factors such as dataset size,image color factor,network structure and hyperparameters.The experimental results show that by increasing the data set,using the color RGB map,adjusting the network structure and hyperparameters,the proposed model is helpful to improve the classification detection accuracy.Compared with the original AlexNet network model,the network model identification accuracy is high,and the recognition accuracy of color image samples is up to 98.5%.At the same time,the image gray level preprocessing is avoided and the efficiency of crack detection is improved.

Key words: Convolutional neural network, Crack detection, Deep learning, Image classification, Rroad pavement