Comparative Study on Concrete Crack Detection of Tunnel Based on Different Deep Learning Algorithms

Abstract

The computer vision inspection of surface cracks in concrete structures has the characteristics of convenient and efficient 24-h on-site inspection. In this paper, the instance segmentation method and semantic segmentation method are used to realize the surface crack recognition of concrete structures, and a concrete crack detection method based on two different deep learning methods is designed. Experiments show that in scenarios where detection accuracy is required, compared with the Mask Region with Convolutional Neural Network method, the U-Net model improves the model segmentation accuracy through up-sampling and skip connection, and its Recall, Kappa, and Dice are increased by 6.88, 1.94, and 7.72%, respectively. In the scene of positioning requirements, Mask Region with Convolutional Neural Network has a better detection effect than U-Net for very thin and inconspicuous cracks, effectively avoiding the situation of missing cracks.