Automatic tunnel lining crack detection via deep learning with generative adversarial network-based data augmentation

Abstract

Aiming at solving the challenges of insufficient data samples and low detection efficiency in tunnel lining crack detection methods based on deep learning, a novel detection approach for tunnel lining crack was proposed, which is based on pruned You Look Only Once v4 (YOLOv4) and Wasserstein Generative Adversarial Network enhanced by Residual Block and Efficient Channel Attention Module (WGAN-RE). In this study, a data augmentation method named WGAN-RE was proposed, which can achieve the automatic generation of crack images to enrich data set. Furthermore, YOLOv4 was selected as the basic model for training, and a pruning algorithm was introduced to lighten the model size, thereby effectively improving the detection speed. Average Precision (AP), F1 Score (F1), model size, and Frames Per Second (FPS) were selected as evaluation indexes of the model performance. Results indicate that the storage space of the pruned YOLOv4 model is only 49.16 MB, which is 80% compressed compared with the model before pruning. In addition, the FPS of the model reaches 40.58f/s, which provides a basis for the real-time detection of tunnel lining cracks. Findings also demonstrate that the F1 score and AP of the pruned YOLOv4 are only 0.77% and 0.50% lower than that before pruning, respectively. Besides, the pruned YOLOv4 is superior in both model accuracy and efficiency compared with YOLOv3, SSD, and Faster RCNN, which indicated that the pruned YOLOv4 model can realize the accurate, fast and intelligent detection of tunnel lining cracks in practical tunnel engineering.