Abstract
Generally, stainless steel has noticeable resistance to corrosion; however, it can still have corrosion because of gases, especially oxygen, and a high saline environment. The visual approach to detecting damage depends on the quality of the images and the knowledge and experience of the experts. Various destructive and non-destructive methods are available to detect damage in steel. Digital image processing techniques are widely accepted as a non-destructive approach for detecting the surface corrosion on iron articles. The selection of the algorithm depends on the environmental parameters, the type of the damage, and the lighting conditions and should also be cost-effective, fast, and with reasonable accuracy. Various image processing techniques in different combinations have been used by researchers to identify risks. This paper presents an adaptive neural network-based approach for the classification of different types of damage in metals. In the present paper, research in this field has been extensively reviewed to identify challenges and successes in this area. For the present work, convolution neural network with a custom kernel has been used to classify different types of corrosion. Pre-trained deep learning algorithms such as VGG, ResNet, and DensNet were used for classification. Finally, a customized VGG19 network with serially fused deep features and optimized hyperparameters were used to improve the classification accuracy.
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Ahuja, S.K., Shukla, M.K., Ravulakollu, K.K. (2021). Neural Network-Based Surface Corrosion Classification on Metal Articles. In: Balas, V.E., Hassanien, A.E., Chakrabarti, S., Mandal, L. (eds) Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. Lecture Notes on Data Engineering and Communications Technologies, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-33-4968-1_10
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