# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 3 Navigating Challenges Related to Underwater Concrete Crack Detection Using Machine Learning  
Working in underwater environments presents unique challenges that require specialized equipment and procedures for inspections. Underwater inspections require specialized diving equipment, including wet suits, diving masks, and tanks for air supply. Divers must be trained in specialized diving techniques, including decompression procedures and safety protocols. Additionally, specialized inspection equipment is necessary, such as underwater cameras, sonar devices, and non-destructive testing equipment.  
One of the most significant difficulties in inspecting underwater concrete structures is detecting cracks. Underwater conditions, such as poor visibility, water pressure, and limited access, make it challenging to detect cracks. Visual inspections by divers are often necessary, but they can be time-consuming, expensive, and potentially hazardous. Advanced techniques such as ultrasonic testing, acoustic emission monitoring, and digital radiography are also used to detect cracks in underwater concrete structures.  
Optical effects can also impact the quality of underwater images captured by unmanned aerial vehicles (UAVs) used for inspections. Optical effects such as refraction, reflection, and attenuation can distort images and impact the accuracy of inspections. To mitigate the impact of these optical effects, UAVs used for underwater inspections must be equipped with specialized cameras and sensors and operated by trained professionals who can account for these optical effects during inspections. Overall, working in underwater environments requires specialized equipment, procedures, and skills to overcome the unique challenges presented by the underwater environment.  
Underwater concrete structures are critical components of many marine-based infrastructures, such as bridges, piers, and offshore oil platforms. Over time, these structures can develop cracks due to various factors, such as corrosion, water pressure, and natural wear and tear. Detecting and monitoring these cracks is essential for ensuring the safety and longevity of the structure. Visual inspection and dye penetrant testing are the traditional methods for crack detection, but they are time-consuming, expensive, and not always accurate. It has already been demonstrated by the works presented in [1; 59] that machine learning approaches offer a very promising solution for detecting cracks in underwater concrete structures.  
One approach to crack detection using machine learning is image processing of underwater concrete surfaces. Images of the concrete surface can be captured by underwater cameras and analyzed using machine learning algorithms to detect the presence of cracks. The first step in this approach is to capture high-quality images of the concrete surface. The images must be clear, well-lit, and taken at a distance close to the concrete surface. Once the images are captured, they are processed using image processing techniques such as edge detection, thresholding, and morphological operations. These techniques allow the machine learning algorithm  
curacy and efficiency, they offer a reliable way to detect and monitor cracks in this critical infrastructure.  
In the next section, we demonstrate the application of deep learning networks for identification of surface cracks in underwater structures. For this purpose, we use AlexNet and SqueezeNet through a transfer learning approach on a large dataset of augmented images. The dataset is generated such that it capture challenges associated with underwater effects.