Abstract
Steel is the most important material in the world of engineering and construction. Modern steelmaking relies on computer vision technologies, like optical cameras to monitor the production and manufacturing processes, which helps companies improve product quality. In this paper, we propose a deep learning method to automatically detect defects on the steel surface. The architecture of our proposed system is separated into two parts. The first part uses a revised version of single shot multibox detector (SSD) model to learn possible defects. Then, deep residual network (ResNet) is used to classify three types of defects: Rust, Scar, and Sponge. The combination of these two models is investigated and discussed thoroughly in this paper. This work additionally employs a real industry dataset to confirm the feasibility of the proposed method and make sure it is applicable to real-world scenarios. The experimental results show that the proposed method can achieve higher precision and recall scores in steel surface defect detection.
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Acknowledgement
This work was supported by the Ministry of Science and Technology, Taiwan, under Grants MOST 106-2218-E-468-001, MOST 107-2221-E-155-048-MY3, and MOST 108-2634-F-008-001, and under Grants from China Steel Corporation RE106728 and RE107705.
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Lin, CY., Chen, CH., Yang, CY., Akhyar, F., Hsu, CY., Ng, HF. (2020). Cascading Convolutional Neural Network for Steel Surface Defect Detection. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_20
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DOI: https://doi.org/10.1007/978-3-030-20454-9_20
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