One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

Marine corrosion research has recently applied advanced image processing techniques. However, the investigation might have been interrupted due to the frequent disruption of corrosion images by certain noises, leading to a significant reduction in analysis accuracy. The comprehensive investigation of corrosion images required the implementation of progressive noise reduction techniques, especially for addressing periodic noise. Hence, the primary objective of this research is to investigate the reduction of periodic noise in ship corrosion images by leveraging a state-of-the-art image processing technique known as the Modified Gaussian Notch-Reject Filter (MGNRF). The methodology includes the selection of noise frequency using a window-based approach and the application of MGNRF for noise reduction. In comparison to other algorithms, the proposed algorithm has achieved promising results, with a mean absolute error (MAE) of 1.287, a remarkable average peak signal-to-noise ratio (PSNR) value of 40.215 and an exceptionally high mean structural similarity index (MSSIM) value of 0.998. This demonstrates the efficiency of the proposed algorithm in efficiently reducing periodic noise and enhancing the quality of ship corrosion images, thus ensuring accurate analysis and making a significant contribution to this critical domain.

The ship's hull is primarily exposed to salt-laden sea spray and high moisture, making it susceptible to corrosion. This has become a major issue in the shipping industry, as corrosion weakens the ship's structural integrity, necessitating expensive maintenance and posing safety concerns. Despite the latest advancements in corrosion maintenance technology, it is essential to detect corrosion as early as possible using computer vision or image processing techniques. However, both approaches have limitations when it comes to detecting weak corrosion boundary and blurry prominent corrosion features. Therefore, the primary objective of this research is to accurately detect corrosion boundaries on the ship's hull using pixel property method. Firstly, data acquisition is performed to identify suspected corrosion regions on the ship's hull. Next, a threshold is calculated by averaging 100 corrosion images of the ship's hull. Afterward, each pixel in the image is analysed to determine the connected components of the corrosion areas. The pixel list and area coordinates are collected after analysing all connected components. Large connected components are merged into a single larger region using morphological closing and flood-fill operations. Finally, the pixel property method is applied using the pixel list and area coordinates to accurately detect corrosion boundaries on the ship's hull. According to the results, the proposed method successfully detected corrosion regions on the ship's hull with a high level of accuracy. Furthermore, the robustness of this method was demonstrated by its ability to segment the weak corrosion boundary and blurry prominent corrosion features on the ship’s hull. These findings indicate that the proposed method is highly accurate for detecting corrosion on ship hulls.