# Computer Vision and Image Processing Approaches for Corrosion Detection
## 6 Discussion and Future Recommendation  
Computer vision and image processing techniques can be considered as a major topic, not only in corrosion detection and maintenance, but also in the manufacturing, aeronautical, power and energy, food and beverage, wastewater management, and construction industries. Most of the papers that were published concerned the topics of the corrosion prediction model, corrosion detection, and corrosion maintenance, and were considered to be computer vision and image processing studies. Complex methodologies, such as in prediction modelling, is one of the most pressing issues and is a frequent topic of research in computer vision and image processing. Computer vision and image processing approaches can provide good insight into the condition of materials. These approaches are convenient to industry, researchers, and academia, as the corrosion can be detected, assessed, analyzed, and maintained from a distance with remote monitoring. The exposed components, materials, and equipment are not taken out of operation, thus, productivity is not affected. Moreover, the lining deterioration, deformation characteristics, and visible internal damage can be located with these approaches. The use of high-resolution images can provide a comprehensive assessment of the corroded area. Automated analysis is conducted on the nodes or edges, and thus, it can reduce the data transmission cost and increase the corrosion detection speed. Digital measurements of a corroded area can be obtained much faster than conventional methods. Immediate results can also be obtained as zooming in on images is possible, and thus, more details for the corrosion analysis can be obtained. IR4.0 (Industrial Revolution 4.0) helps the industry, researchers, and academia to use more advanced technologies for corrosion detection, such as the advanced sensor, data analytics, signal processing, machine learning, big data, and the internet of things (IoT). The computer vision approach certainly needs imaging equipment to monitor or capture the suspected corroded area. Advanced sensors with a 5G network can complement the computer vision approach for more accurate and efficient corrosion detection. Advanced and intelligent sensors, such as gas sensors [102; 103], temperature and humidity sensors [104; 105], and pH sensors [106; 107], have been developed, and they can intelligently capture and analyze the important corrosion parameters at the site. These sensors are well equipped with machine learning, which can improve the accuracy and efficiency of their sensing and analyzing capabilities. Machine learning, particularly deep learning, has provided a great opportunity for corrosion prediction, assessment, and detection in terms of the applicability of processing images and video data. The use and integration of the current equipment with IR4.0 technology are fully recommended for corrosion detection [108]. The future integration between LiDAR and infrared technology in remote sensing can provide more details and information for corrosion detection, assessment, and maintenance. Remote sensing imagery contains spatial, temporal, spectral, geometric, and radiometric resolutions which can be used to extract more information from the respective corrosion images. Hence, considering recent advances in IR4.0, a high level of computer vision, image processing, and artificial intelligence can be achieved for corrosion detection, assessment, and prediction. In addition, the combination of computer vision, micro-indentation mapping, pH profiling, scanning electron microscopy, and neutron tomography can be used as a novel methodology for microbiological corrosion detection. So far, there is no standard evaluation approach for microbiological corrosion [109]. Microbiological corrosion is common in sewage pipes where acids are formed from chemical compounds and acidophilic bacterial colonies.