An Autonomous Inspection Method for Pitting Detection Using Deep Learning

The corrosion inspection process in ship tanks used by the oil industry for the production, storage, and disposal of oil, which is known as Floating Production Storage and Offloading (FPSO), is predominantly manual. It requires a long production downtime, and is an unhealthy job for inspectors. In the literature, some works proposed methods for corrosion segmentation. However, none of them classifies the level of corrosion in accordance with the International Association of Classification Societies (IACS) standard. This work proposes the use of U-Net-based network for segmentation of pitting corrosion, and also provides a corrosion level analysis algorithm relating the identified pitting to the IACS standard. Furthermore, data augmentation methods are adopted to make the dataset more diversified, aiming to generalize the neural network learning. The results indicate a mean squared error of only 0.1639 using the proposed method, and an intersection-of-union of 0.9453. In addition, we compared our method with classical methods such as Canny, Laplacian, Otsu, and Sobel methods, where a relevant advantage is obtained with U-Net.