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.

Corrosion is a natural phenomenon that deteriorates and damages the surface of metallic material. Over time, the surface of the material deteriorates due to electrochemical reactions with the surrounding environment. If corrosion is not identified early on, it can become a major financial burden for industries, costing billions of dollars. Despite swift technological developments, preventing and maintaining corrosion progression with reactive maintenance remains difficult. Due to that, predictive maintenance has been developed to predict the deterioration, degradation, and fault over the remaining useful life of the material by using real-time data, historical data, simulation, modelling, and failure probability. Predictive maintenance allows inspectors to monitor the health and predict the corrosion level of the material. However, it is hard to predict the unexpected degradation of the material from the developed prediction model without considering the harsh environment and other external factors. Hence, there is a need to investigate these problems and their effect on predictive maintenance for corrosion detection and maintenance. Therefore, this paper reviews and compares the state-of-the-art predictive maintenance solutions developed to solve corrosion issues in various applications, industries, and academic research. The challenges and opportunities for the predictive maintenance application of corrosion detection and maintenance are also presented. This review will provide new and additional knowledge that can be used to develop prediction models for corrosion detection and maintenance, which will help prevent unexpected failures.

Many studies have been conducted to help improve student performance in academics. Either tested practically or scientifically, both have been implemented. Until now, there has been no ultimate teaching style to ensure student performance in academics while maintaining their motivation to study. This is because students differ in their natural ways of learning. This study was conducted at the University of Malaysia Terengganu (UMT), among Nautical Science and Maritime Transportation students. In this study, research on improving student performance in Maritime English has been conducted. The current system of the learning and teaching process of Maritime English at UMT was determined in this research and, in order to evaluate the current proficiency in Maritime English, content analysis has been used to analyze their final examination paper. Additionally, interview sessions were conducted with a selected expert to obtain a recommendation on how to improve the performance of students in Maritime English. The subjects were among the first-year students of the Bachelor of Nautical Science and Maritime Transportation program. At the end of the research, this paper gives a recommendation on how to improve performance in the learning and teaching processes of Maritime English courses for students at maritime universities around the world.