Automatic defect detection is a continuously advancing topic in research that lacks an effective market implementation in the case of shipping industry. Recently, there exists a boost for autonomous inspection of industrial, transport and building infrastructure through the synergy of robotics and computer vision. A first approach on rust detection on vessels has been developed within the MINOAS and INCASS frameworks.
The automation of corrosion and crack detection via non-contact and non-destructive techniques, instead of electrochemical methods, is an elaborate research problem. Typical methods perform image analysis on RGB data from metal surfaces. The various employed methods are mainly categorized in two groups; the ones based on an automated detection (e.g. on the wavelet domain, thresholding, spectral band combinations and analysis, image segmentation, boundary or shape analysis); and the methods based on image classification procedures.
ROBINS will promote the state-of-the-art in research and bring productive cutting-edge technologies closer to shipping market via the robotic integration of advanced defect detection and recording. Novel platforms of integrated lightweight sensors will be built, specifically oriented to defect detection. These platforms will take advantage of the modular structure of the proposed robots, aiming at adaptation on each case.
Simple image feature extraction may not be enough to detect damage in ship structure due to complex lighting conditions and great diversity of other conditions within data obtained from different robots. In this scenario, manual definition of features for defect representation is not feasible. Thus, digital image based defect detection should be built on a basis of highly robust machine-learning approach such as convolutional neural networks (CNN), which already showed good results in problem of object classification in photographs and looks promising for industrial inspection applications. In contrast to manually designed image processing solutions, deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge.
Development of deep learning-based approaches for corrosion detection by means of bounding box regression.
Development of deep learning-based approaches for corrosion detection by means of semantic segmentation.