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.

The functional performance of concrete structures degrades over time as a result of continuous loads, stress fatigue, and external environmental changes. Thus, periodic diagnoses and inspections are essential because such conditions can eventually lead to disaster. Hence, the detection of cracks in concrete is a key component of structural management. In recent years, deep-learning-based computer vision technologies have emerged as a promising trend and have been actively used for crack detection. Unfortunately, the performance of existing crack detection technologies decreases under environmental conditions that vary widely. To resolve this issue, we propose a new deep neural network that applies an optimal mixing ratio of training data to improve recognition performance alongside an adversarial learning-based balanced ensemble discriminator network. Furthermore, a method to reconstruct the 3-dimensional shape of cracks is proposed using a stereo-vision-based triangulation measurement technique that determines the size of detected cracks. Experimental results show that the proposed algorithm achieved a crack detection performance with a mean intersection-over-union of 84.53% and an F1 score of 82.91%. The proposed inspection technology for concrete structures is expected to be implemented in the future in connection with various automation techniques.

The aging of concrete structures is a threat to public safety; therefore, maintenance and repair of these structures have been highly emphasized. However, regular inspections to detect concrete cracks that rely on operators lack objectivity and consume a lot of time. To overcome this limitation, high-resolution image processing and deep learning have been adopted. Nevertheless, cracks on structure surfaces are still challenging to detect owing to the variety of shapes of cracks and the dependence of recognition performance on image conditions. Herein, we propose a new concrete crack detection method that applies the semantic segmentation technique using 1196 concrete crack images and labeled images produced in this study. A new segmentation algorithm is developed using a hierarchical convolutional neural network to improve speed, and a multi-loss update method is proposed to improve accuracy. The performance of the proposed network is evaluated in terms of accuracy and speed. The results show that the proposed network produces a 2.165% increase in the intersection over union of crack, 65.90% decrease in the average inference time, and 99.90% decrease in the number of parameters compared with the best accuracy results using existing segmentation networks. It is expected that the application of this improved crack detection method will result in faster and more accurate crack detection and, consequently, improved safety, thereby making it suitable for application in structure safety inspections.

With an aging society, the demand for personal mobility for disabled and aging people is increasing. As of 2017, the number of electric wheelchairs in Korea was 90,000 according to the domestic government statistics and has since increased continuously. However, people with disabilities and seniors are more likely to be involved in accidents while driving because their judgment and coordination are inferior to those of ordinary people. One of the factors that could lead to accidents is the interference in the vehicle-steering control owing to unbalanced road-surface conditions. In this paper, we introduce a lightweight semantic segmentation algorithm that can recognize the area with road-surface damage through images at high speed to prevent occurrence of such accidents. To test the algorithm, an experiment was conducted in which more than 1,500 training data and 150 validation data, including road-surface damage, were newly created. By using the data, we propose a new deep neural network composed of only encoder type, unlike the auto-encoding type consisting of encoder and decoder. To evaluate the performance of the proposed algorithm, we considered four metrics of the accuracy and two metrics of the speed. Unlike the conventional method, this deep neural network method shows improvement in all of the accuracy index, a 85.7% decrease in parameters, and an 6.1% increase in computational speed. The application of such a high-speed algorithm is expected to improve safety in personal transportation.

To develop countermeasures against the inevitable aging of tunnels, accurate inspection is critical for ensuring stable tunnel services. Tunnels are large-scale infrastructures, whereas the cracks in such tunnels are small-scale objects, necessitating the development of methods capable of rapidly detecting cracks in a wide field of view. Therefore, this study proposes a new learning structure based on a segmentation network. This structure includes super-resolution and generative adversarial networks, which contribute to improved detection performance with robustness to input data. Furthermore, a method for the effective construction of training data for the application of the proposed structure is presented. Subsequently, the performance of the method over 1,606 crack images with randomly degraded qualities is evaluated. The proposed structure presents improved crack intersection over union and F1-scores of 63.686% and 77.811%, respectively, and low variances of 0.9008 and 0.5015 compared to the original structure. The results presented herein indicate the possible application of the proposed accurate condition inspection technology to tunnel maintenance in the future.