# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 1 Introduction  
Infrastructure, such as highways, road networks, bridges, and dams, are key assets of any society. Structural defects caused by ageing and environmental variations can adversely affect the reliability of structures highlighting the importance of infrastructure monitoring strategies. By and large, infrastructure management has the fundamental goal of preserving and extending the service life of long-standing structures [1]. To maintain the integrity of structures, it is important to detect the onset of any defect. Cracks are the most common defects on pavements and the surface of concrete structures and this structural degradation can potentially threaten safety and reduce service life [2]. The first and most common procedure to evaluate the health state of a structure is to perform visual inspection, which is costly and labour-intensive. Moreover, manual inspection requires highly trained experts in the field and is subject to the judgement of the specialist. These limitations have motivated work on automatic crack-detection approaches in both industry and academia [3, 4]. Image-based approaches are considered to be more cost-effective because of the widespread availability of cameras and smartphones [5]. To automate the image-based crack detection procedure, computer vision techniques have been found to be effective and their application has become a research challenge in the past decades [2]. The main step of performing crack detection using computer vision techniques is to extract crack sensitive features which can be done by leveraging either Image Processing techniques (IPTs) or deep architectures. Accordingly, image-based crack detection studies are generally categorised into (i) manual and (ii) automatic feature extraction-based approaches. Simply speaking, crack sensitive features are manually extracted via the application of traditional IPTs. Early studies concentrated on the application of methods such as edge detectors [6], morphological operations [7], and thresholding [8]. However, recent studies have taken advantage of the classification ability of classical machine learning algorithms. The extracted handcrafted features are fed to classical machine learning algorithms such as support vector machines (SVMs) [9], Random Forest [10], and neural networks (NNs) [11] to improve the accuracy of approaches in this category. Despite the wealth of research in this category, given that the main performance comes from the chosen handcrafted feature, handcrafted feature-based crack detection approaches are still sensitive to noise and different illumination conditions [4]. This problem has led to the application of deep architectures for the task of crack detection where no handcrafted features are involved. Deep learning is a branch of machine learning in which NNs with deep layers are used to gradually perform feature extraction and the architecture itself decides which features are relevant [12]. Numerous deep learning-based crack detection studies have been proposed which are categorised into studies based on (i) image classification (IC), (ii) object recognition (OR), and (iii) semantic segmentation (SS) settings [13]. Mainly, the difference between each setting is the level at which the crack detection is performed (e.g., image-, image patch-, or pixel-level). To gain insight into the application trend of each settings in the crack detection area, Figure 1 is provided. As can be seen, the application of SS has become the most popular setting in the crack detection area in the past years. It is obvious that the number of published studies based on deep learning architectures in the crack detection area is growing fast. Thus more detailed review and categorization of these studies at deeper levels and from different points of view is necessary, to help interested scholars to more fully understand and compare the proposed approaches. To the best of the authors' knowledge, only three review papers have been published in this research area.