Abstract
In this paper, we report an enhancement in the functionality of footing machine using deep neural networks. In our earlier work, we have replaced a large array of sensors, traditionally used on footing machines, with a roof-mounted camera to ensure the robust operation of the machine and designed an image processing-based algorithm to measure the area of irregular leather pieces. However, it is also desired that footing machine be able to separate the defective and non-defective leather pieces. Subsequently, we have added this feature to the footing machine using deep neural networks and report the results of proposed enhancement in this paper. Owing to the availability of small dataset, pre-trained deep network models are considered. Results obtained through MATALB environment show the validity of the proposed enhancement.
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Haroon, A. et al. (2023). Enhanced Functionality of Footing Machine through Deep Learning. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_1
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