# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
###### Abstract  
This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks' performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.  
underwater; crack detection; machine learning; transfer learning; augmentation; non-destructive testing; safety; reliability +
Footnote †: journal: Medical  
* Non-destructive testing techniques: Techniques such as ultrasonic testing and acoustic emission monitoring may be used to evaluate the interior condition of a concrete structure and detect cracks or other flaws. These methods rely on the analysis of sound waves or emitted signals to identify potential issues. However, they require specialized equipment and expertise to perform accurately. The time required to acquire and process data is long. Often, the cost of such data acquisition is very high as well.
* Advanced technologies: Underwater drones and robots equipped with cameras and sensors are emerging as valuable tools for crack detection and monitoring. These autonomous or remotely operated devices can access hard-to-reach areas, capture high-resolution images or videos, and collect data on the condition of the structures. This technology offers improved accessibility and accuracy in crack detection. The technique presented in this paper is an addition to this type of technology for underwater inspections.  
In the present time, the amount of underwater infrastructure has increased many-fold, including internet cables, electric cables linked to offshore wind mills, and oil and gas pipelines, among other important infrastructure. Implementing reliable methods for detecting and monitoring cracks in underwater concrete structures is crucial for ensuring their long-term durability and safety. While significant research has been conducted on crack detection systems for onshore and above-water structures [1; 2], less attention has been given to underwater crack detection. The work presented in this paper focuses specifically on addressing this gap and developing effective crack detection methods for underwater concrete structures. By improving our ability to detect and monitor cracks in underwater concrete structures, we can identify issues early, implement appropriate repairs or maintenance, and ensure the continued functionality and safety of these vital underwater assets.  
The paper is structured as follows: An overview of different techniques used in underwater concrete crack detection is presented in Section 2. Challenges in underwater concrete crack detection and mitigation methods are discussed in Section 3. Section 4 presents an overview of the data set used in this paper to demonstrate use of a machine-learning-based approach for underwater crack detection. A machine-learning-based underwater crack detection system and a series of test cases are presented in Section 5. Conclusions follows in Section 6.