# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 2 An Overview of the Techniques Used for Underwater Concrete Crack Detection
### Deep-Learning-Based Methods: Image Analysis of Cracks Using Deep Learning  
Recently, neural networks have been increasingly applied to the quality of surface water estimation, utilizing combined optical and microwave data. These networks excel in approximating nonlinear transfer functions and, as such, research is involved for extracting data from water sample locations and analyzing digital data through various transformations [30]. In [31], A application for Windows was created to simulate and evaluate optical observations in aquatic settings. Surface water detection is important for understanding flood hazards and potential damage to infrastructure and ecosystems. In [32; 33] the applications of satellite remote sensing and its limitations in detecting and monitoring surface water, mapping, and parameter estimation were discussed. An unsupervised method for identifying fractures in underwater concrete structures was offered in [34]. For eliminating outliers, the approach depends on local feature clustering utilizing K-medians on Haralick texture characteristics with a dual Gaussian distribution. Detecting and classifying cracks in underwater dam structures based on sonar images is a difficult task due to the complexity of underwater environments and the random and diverse nature of cracks, as well as the low resolution of sonar images. In [35], a clustering analysis was performed on a 3D feature space to obtain crack fragments, which were then connected using an improved tensor voting method. Ref. [36] presented an alternate technique for identifying underwater dam breaches in sonar imagery. The cracked block tree (BT) approach comprises pre-processing low-resolution sonar pictures, breaking them down into pieces for grouping analysis, and combining the crack segments with dynamic fragments of fractures based on tensor voting. In [37], a two-phase system was proposedfor robust crack detection in concrete and pipelines inspections using remotely operated vehicles. Ref. [38] proposed a two-step approach for automatically identifying concrete fractures in aquatic situations. In the first step, the images were pre-processed through illumination balancing and image smoothing, and in the second step, a convolutional neural network (CNN) was used for crack detection. In [39], a novel algorithm was proposed that generated a 3D spatial surface from the intensity values of a 2D image. The cracks were identified as \"ditches\" in the 3D surface, and their characteristics were analyzed using space curvatures. A BP neural network was then used to identify the crack objects. In [40], an artificial colony of bees algorithm-based edge extraction methodology was proposed. To improve the weak-object border gray contrast, an adaptive enhancement approach was applied, and a method of optimization using border direction information was presented to improve the edge extraction efficacy. Ref. [41] presented the establishment of a free aquatic light and turbid images repository (ULTIR) to assess the efficacy of image-based approaches for underwater structural evaluation. In [42], an automatic crack detection method using image processing was proposed. The approach created an augmented picture based on turbidity meter absorbance and eliminated the background component. Crack detection was performed using a decision tree learning algorithm. Ref. [43] presented a unique automated dam fracture detection technique that utilized local-global clustering analysis. Using photographs, the system can precisely and rapidly detect faults on dam surfaces, decreasing human subjectivity. In [44], a Tactile Imaging System for Underwater Inspection (TISUE) was conceived, prototyped, and tested. The device combined an elastomer-enabled contact-based optic sensor with specially designed artificial illumination to provide high-resolution and high-quality pictures of the structural damage to the surface in a turbid water environment.