This paper involves the automation of a visual characterisation technique for corrosion in marine vessels, as it appears in the hull preventive coatings of marine vessels and their surfaces. We propose a module that maximizes the utilisation of features learned by a deep convolutional neural network to identify areas of corrosion and segment pixels in regions of inspection interest for corrosion detection. Our segmentation module is based on Eigen tree decomposition and information-based decision criteria in order to produce specific corroded spots—regions of interest. To assess performance and compare it with our method, we utilize several state-of-the-art deep learning architectures.The results indicate that our method achieves higher accuracy and precision while maintaining the significance score across the entire dataset. To the best of our knowledge, this is the first Eigen tree-based module in the literature in the context of trained neural network predictors for classifying corrosion in marine vessel images.

In this paper we propose a module that uses features learned by a deep convolutional neural network to infer areas of corrosion and segment pixels to corrosion areas of inspection interest. Our segmentation module is based on eigen tree decomposition and information based decision criteria. To interrogate performance we use several state-of-the-art deep learning architectures and compare to our approach. The results indicate that our method produces better results in terms of accuracy and precision, whilst maintaining good (f-score) significance over the entire dataset.