A Review on the Development of Deep Learning Based Concrete Crack Detection

Under the background of rapid development of artificial intelligence, computer vision technology has entered a new stage, and the concrete crack detection method based on deep learning can effectively improve the accuracy and precision of detection. Semantic segmentation has the characteristics of pixel-level classification, which is analysed and found to have strong applicability in concrete crack detection work. In this paper, the research results of deep learning based on FCN, U-Net and ResNet architectures are summarized, and found that through the introduction of the combination of image preprocessing and morphological post-processing, the pixel accuracy rate of C-FCN, the recall rate, and the intersection-and-comparison FCN are improved by 5.61%, 16.56% and 13.22%, respectively. The use of a hybrid loss function combining Dice Loss and cross-entropy can effectively address the issue of imbalance between foreground and background pixel samples. After optimization, the IoU and F1 of U-Net are increased by 5.41% and 5.19% respectively compared to before. The residual block short-circuiting mechanism improves the gradient propagation, accelerates the training process as well as improves the performance of the model, which contains residual blocks AcNet improves the F1 by 1.17% and 2.43% and the OR by 1.35% and 2.78% compared to U-Net and RCF, respectively. Convolutional neural network model building is relatively flexible, and it can be optimised in terms of parameter update method, activation function, loss function, etc. According to the actual situation, this paper summarises the contents of the three important parts of deep learning model building, data set collection and model evaluation, with a view to providing some help for subsequent related research.