Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Typically, the operational lifetime of underground concrete structures is several decades. At present, many such structures are approaching their original life expectancy. In this stage, the essential functionality of the structures may be considerably degraded, leading to various safety hazards such as collapse roof and tunnel flooding. In general, to overcome such problems, the maintenance of underground structures has been conducted through manual subjective inspections so far. However, recently, several objective inspection technologies have been actively developed by fusing artificial intelligence and imaging techniques recently. In particular, deep learning algorithms have been developed to detect concrete cracks, based on a large amount of data for supervised learning, including numerous labeled images. Such data acquisition requires considerable time and effort. To reduce these costs, in this study, multiscale and adversarial learning techniques were applied to realize crack detection. A total of 1,200 labeled data and 3,000 unlabeled data were used to implement and verify the proposed method. The multiscale segmentation neural network, discriminator neural network, and adversarial learning technique were used to realize accurate crack detection, enhance the learning performance, and ensure the efficiency of training data, respectively. The resulting algorithm had a pixel accuracy, mean intersection over union, frequency weighted intersection over union, and F1 score of 98.176%, 88.936%, 96.525%, and 88.789%, respectively. The proposed technique can be used to examine the conditions to ensure the safe maintenance of aging structures.