## 1 Introduction
However, the discussed approaches are not robust enough in some special cases when images are extremely noisy, or when the concrete surfaces are very rough and contain complex crack patterns intertwined with high-intensity shadows. More recently, deep-learning-based methods, using ANN- and CNN-type neural networks, have been applied to automatically process the images for crack/failure identification in concrete onshore installations [8; 9; 10; 11; 12]. With the development of artificial intelligence and deep learning technologies, several applications, based on the use of CNN-type neural networks, for the identification of crack on concrete surfaces, have emerged. A detailed summary of such studies is presented in Table 1. It can be noted that many of these methods could be characterized by a high classification accuracy. Application of the deep learning methodology is a relatively new approach as it could be seen from the list of publications provided in Table 1. Most of the published data spans from a period ranging from 2011 to 2021. A chronological summary of such studies is presented further. In [13], the authors use the back-propagation neural network that is trained on 105 images of various concrete structures, and the trained network is tested on 120 new images. The recognition rate of the images with and without cracks is 90% and 92%, respectively. A methodology for crack detection and identification using the object detection method as well as fuzzy logic and ANN-based models is published in [14]. In this study, the authors utilize 205 images of 256 \(\times\) 256 resolution and claim a very high crack detection accuracy of 96%. It is important to note, however, that a very limited number of images is used in both of the previously mentioned studies. An image segmentation method for the automatic peak detection enabling crack identification is presented in [15]. A review of over 50 publications related to the application of image analysis for crack detection is published in [4]. First extensive dataset of over 40,000 images of 256 \(\times\) 256 resolution is utilized in [9]. The authors apply a CNN-based deep-learning method for crack detection and achieve very high accuracy of over 98%. In [16; 17], CNN-based methods are applied to the same set of 40,000 images of 227 \(\times\) 227 resolution, both claiming good prediction accuracy of over 99% and 90% respectively. Differently from the previous examples, in [18], the authors have used 60,000 images of a very high resolution (3120 \(\times\) 4160), for training and testing of the applied CNN-based crack identification method with a very high prediction accuracy of about 99%. The authors of [8] use the same dataset presented in [9] and further improve the accuracy of the CNN to 99.9%. A comparison of different deep-learning networks is performed in [19; 20] and uses over 20,000 images of higher resolution of about 1024 \(\times\) 1024. The authors of [21; 22] also apply CNN for crack detection and present a comparison of the new CNN-based method with R-CNN and U-Net learning architectures. However, the achieved accuracy is rather low (approximately 82.5%). A novel approach for crack detection and identification utilizing long and short-term-memory-based deep-learning convolutional neural networks (CNN-LSTM) is presented in [23].