# Computer Vision and Image Processing Approaches for Corrosion Detection
## 5 Challenges in Corrosion Detection
Operation interruptions will affect production and runtime, which can reduce productivity. Moreover, there is a significant problem in terms of replacing the infrared detectors and sensors in small spaces and confined spaces such as in the ship engine room and wastewater pipeline. The detectors and sensors might stop working and break down due to longevity and durability issues. In terms of ship structures and pipelines, the main concern is how to reach these structures in order to capture images documenting their physical condition [100]. The most affected areas on the ship, such as the engine room and ship hull, are dangerous and difficult to reach [101]. Moreover, the oil and gas pipelines are isolated and located underwater. The wastewater pipelines are also too small for humans to reach. Consequently, robotic vehicles and drones can be used to reach those places. However, the cost of this equipment is not cheap, and it might require a significant number of robotic vehicles and cameras to be installed on those structures. Moreover, the ultrasound and GPR can be employed for concrete corrosion detection. Both types of equipment can be considered as being part of the machine vision and image processing approaches. The boundary layer depth and concrete flaws can be detected microbiologically. However, the sensors used in both methods can be constrained by sound coupling, inaccurate predictive models, and moisture level. Hence, there is a need to address these problems as the aggregate location of corrosion cannot be accurately detected, and thus, it can produce a false corrosion segmentation. The combination of digital CCTV with infrared scanning, sonar, and radar can also be used for corrosion inspection and detection. These imaging techniques can be enhanced using multiple-angles, fisheye lenses, and higher-resolution cameras. These methods can be employed in small spaces, confined spaces, and out-of-reach areas. Thus, the presence of a corrosion inspector is minimized. Nevertheless, these methods are error-prone as they need to be mounted on a robot crawler which is reliant on the operator's expertise. On the other hand, advances in robotic technology, machine learning, and LiDAR (light detection and ranging) sensors can help operators to minimize these issues. LiDAR with point cloud can be used for corrosion detection on the ship hull. This imaging method can estimate the corroded areas by using an integrated 2D camera and RGB values on the ship structures. However, this technique relies heavily on the reflected data of the material surface. Consequently, some corrosion progresses on the material surface cannot be detected, especially on blurry areas, weak boundaries, and disoriented surfaces. This method is best suited for use with geometrical deviation surfaces. Computed tomography with nuclear sources or gamma rays is another method for corrosion detection. This technique uses a radioactive source, such as cobalt and cesium, to develop and reconstruct a 3D image profile of an object or material. Radioactive sources can penetrate thick materials or insulation for corrosion detection. Nevertheless, radioactive particles are very dangerous and must be handled and prepared by a competent person with adequate certifications. This competent person must also have a fundamental knowledge of computer vision, image processing, and artificial intelligence. Moreover, specialized training is required before the person can be employed, which can increase upskilling costs and training costs. Data complexity remains a critical issue in the implementation of computer vision and image processing approaches in corrosion studies. The low and medium quality of images and videos can produce false corrosion detection results. Noise, distortion, and attenuation also pose critical challenges for object detection and classification. Consequently, some data from images must be reconstructed and converted into frequency domains or time domains in order to obtain the relevant information.