Abstract
In manufacturing industry, the surface imperfections in industrial objects have a substantial influence on product quality, safety, and aesthetics. Steel is considered as the most widely used industrial item due to its wide range of applications. However, quality control is considered as the most crucial stage in the manufacturing industry in order to detect damage in the steel sheet. Even today, many industries perform quality control operations manually. Since, manual works consume more time, a suitable computer vision technology has been developed to replace the human labor. The proposed automated technology uses a deep learning approach called convolutional neural networks (CNN) to identify and detect surface imperfections in the industrial steel material autonomously. The proposed study attempts to develop a convolutional neural network (CNN) model to identify different types of faults in the steel sheets.
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Sambath, M., Sai Bhargav Reddy, C., Kalyan Reddy, Y., Mohit Sairam Reddy, M., Kathiravan, M., Ravi, S. (2023). Deep Learning-Based Quality Inspection System for Steel Sheet. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_16
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DOI: https://doi.org/10.1007/978-981-19-6004-8_16
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Online ISBN: 978-981-19-6004-8
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