## 4 Discussion  
In this paper, a brief review of deep-learning-based concrete-crack detection studies published in literature is presented. Difficulties associated with automatic concrete-crack detection in presence of shadows effects are highlighted, with help of a series of experiments, involving the application of three different classification neural networks (AlexNet, SqueezeNet and VGG16). The experiments demonstrate that the deep-learning methods trained on images acquired in laboratory conditions are incapable of accurate detection of cracks in the concrete images with shadows. Furthermore, it is shown that even the application of pre-processing techniques for shadow removal does not always improve the detection accuracy since it can lead to deterioration in image quality (which makes the resulting concrete images unsuitable for further image processing). This paper highlights difficulties associated with the detection and identification of cracks in the real-life concrete images. This paper also highlights the limitations of the published studies, which are only utilizing the images taken in ideal conditions of onshore concrete structures in controlled illumination (laboratory) conditions. Results presented in this paper show that artificial neural networks trained on ideal laboratory images are only good enough to classify ideal images, and accuracy of classification fails significantly in presence of noises, particularly shadows. Both, conventional as well as deep-learning-based methods have some issues related to robust crack identification in extreme conditions. Some of these issues might be solved by fine-tuning the parameters of these methods in order to adapt them to a specific situation. The literature review and computational tests performed in this article show that one single technique capable of identifying concrete cracks when digital images are contaminated by extreme noise, or when concrete surfaces are rough and contain complex crack patterns with high intensity shadows, does not exist. This task gets even more challenging when the noise, the roughness of the surface, and the shadow intensity and its shape are variable in the processed dataset. In such a case, fine-tuning of the parameters of the existing techniques becomes a very challenging task. In order to achieve full automation of the crack detection via the deep-learning approaches, with the use of unmanned aerial vehicles (UAV) or drones, the methodology must be robust enough to deal with real-life imaging affected by various illumination conditions, randomness, and irregular sizes, of crack shapes, visual noises and shadows. Challenges presented in this paper highlight the need for the development of a deep-learning framework that could perform seamless classification of concrete surface crack in presence of shadows and random visual noises. The challenges could be mitigated and the real time classification of concrete surfaces could be achieved by development of a deep learning network, which is trained using an exhaustive data base of more realistic concrete crack images. M.P., contributed to manuscript preparation, developed shadow detection and removal codes and developed crack classification models. I.T., contributed to manuscript preparation and preparation of augmented images used in testing and training of the deep-learning networks. P.P., contributed to manuscript preparation, image augmentation techniques and preparation of classification image data sets. M.L., contributed to development of crack classification models used in paper. U.O., contributed to preparation of augmented images used in testing and training of the deep-learning networks. M.R., contributed to manuscript preparation, guidance of overall structure of the paper and internal critical review and assessment of approaches presented in the paper. All authors have read and agreed to the published version of the manuscript. This research is funded by KTU Science Investment Project, project grant No. PP61/21.