# Computer Vision and Image Processing Approaches for Corrosion Detection
## 1 Introduction  
Corrosion or rust is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment [1]. Additionally, corrosion presents considerable threats in both the short and long term; this costs billions of dollars because of its ability to create failures, leakages, and damage to materials, equipment, and environment. The drawbacks of corrosion, such as expensive overdesign, costly maintenance, reduced efficiency, product contamination, plant shutdown, production loss, nuclear hazards, and heat transfer reduction, have burdened a lot of industries [2]. It also can threaten human health by damaging the respiratory tract, digestive tract, skin, and eyes [3]. Human tissues can be destroyed and burned on contact, and corrosion can cause permanent lung injury, blindness, scarring, and even death [4]. Computer vision involves developing algorithms and techniques that enable machines to simulate or replicate human vision using machine learning. The machine or computer has the ability to perceive and analyze patterns using visual inputs. It imitates the intricacy of the human visual system and analyzes inputs based on attributes like color, size, texture, and other factors. Computer vision is used for corrosion detection since it can locate and classify the corroded area in images along with the other machine learning techniques. Other examples of computer vision applications include face detection, car detection, andiris classification. Moreover, image processing refers to a technique that involves adjusting various attributes and parameters of images in order to improve their quality, enhance their appearance, and modify them in accordance with specific needs. There are numerous techniques available to transform images. These digital images contain specific values and positional elements, including visual elements, picture elements and pixels. Therefore, image processing has practical applications in detecting corrosion because its techniques are capable of segmenting, filtering, sharpening, and enhancing corroded areas in images. Examples of image processing applications include illumination correction, noise reduction, contrast enhancement, and image rescaling. The detection of corrosion through computer vision and image processing involves five primary processes: image acquisition, image pre-processing, image segmentation, feature extraction, and image classification. Despite the fact that chemical and physical tests are highly efficient for corrosion detection, it remains difficult to implement these methods in confined spaces, narrow pipelines, small spaces, dangerous areas, and large surface areas. Consequently, computer vision and image processing approaches have been adopted to detect, assess, and predict corrosion progression, deterioration, and degradation on equipment and materials. Both approaches are inexpensive, reasonably accurate, fast, reliable, and can be implemented on large and wide areas such as bridges, ships, buildings, etc. However, it is hard to detect and predict corrosion in images captured in different environments, with different levels of quality, and different parameters, using a single technique. One computer vision or image processing technique is not enough to manage all types of corrosion images without considering the harsh environment and other external factors. Hence, there is a need to investigate various computer vision and image processing techniques such as color analysis, texture detection, denoising, registration, edge and line detection, filtering, pixelation, clustering, classification, segmentation, wavelet transformation, pattern recognition, morphological operation, and image enhancement that can be integrated or combined for corrosion detection, as well as the assessment and prediction of different corrosion conditions.