Abstract
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area.
Highlights
Infrastructure, such as highways, road networks, bridges, and dams, are key assets of any society
Given the provided definition for deep architectures and the proposed framework by Farrar and Worden [16], the procedure of developing a deep learning-based crack detection system can be summarised in three steps where feature extraction and statistical modelling are performed in one step
In deep crack detection approaches based on the image classification (IC) settings, the decision is limited to image/image patch-level and the trained architecture decides whether the new input is a crack contained one or not
Summary
Infrastructure, such as highways, road networks, bridges, and dams, are key assets of any society. 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. In [15], in addition to reviewing deep learning-based crack detection approaches, a comparison between three types of 3D data representation is performed.
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