Pavement crack detection algorithm based on generative adversarial network and convolutional neural network under small samples

• Published in: Measurement : journal of the International Measurement Confederation Vol. 196; p. 111219
• Main Authors: Xu, Boqiang, Liu, Chao
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 15.06.2022; Elsevier Science Ltd
• Subjects: Accuracy; Algorithms; Artificial neural networks; Computer vision; Convolutional neural network; Crack identification; Cracks; Datasets; Deep learning; Generative adversarial network; Generative adversarial networks; Machine learning; Neural networks; Pavements; Sensors; Training; Transfer learning; UAV; Unmanned aerial vehicles; Crack identification; UAV; Transfer learning; Convolutional neural network; Generative adversarial network
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.111219

•A small sample pavement crack detection framework based on GAN and CNN.•The image generated by the proposed GAN model can retain the high-frequency information and be directly used in CNN model.•The detection method based on CAM enables the classification model to provide semantic information and expands the detection ability of the classification model. Pavement crack detection methods based on deep learning and computer vision can greatly improve detection efficiency and accuracy, but in many cases the data in training set is lacking or uneven, making it insufficient to train an accurate detection model. This paper proposes a detection method under small samples, which is composed of two steps. First, with a generative adversarial network (GAN) constructed, the small sample data set of pavement cracks taken by unmanned aerial vehicle (UAV) is used as the training set and the GAN model is trained. The best trained model is used for generation of new images. Second, original small-sample data set is expanded by images generated by the GAN model, and a convolutional neural network (CNN) model is constructed at the same time. Then, data set before and after the expansion is trained and tested by the method of transfer learning to verify the effectiveness of expanded data separately. It has been proved that, compared with the unexpanded data set, CNN model trained after expansion improves the test set detection accuracy from 80.75% to 91.61%, which is regarded as a significant improvement. In addition, this paper also uses class activation map (CAM) to visually evaluate CNN model, and expands the detection ability of classification model.