Abstract
Computer vision technology is widely implemented in electronic manufacturing industry to detect the defects on printed circuit board (PCB). However, the wrong attachment of electronic components is a notable issue leading to poor production efficiency in PCB assembly line. In this work, a computer-vision-based system is proposed with the use of deep learning neural network technique to perform the detection of integrated circuit (IC). The trained deep learning model is imported to mobile device with the features of object detection and text recognition on ICs. The experiments reported that the proposed technique has promising component recognition performance in detecting real ICs image datasets.
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Acknowledgements
This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme under Project Proj-FRGS/1/2019/TK04/UCSI/02/1.
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Voon, Y.N., Ang, K.M., Chong, Y.H., Lim, W.H., Tiang, S.S. (2022). Computer-Vision-Based Integrated Circuit Recognition Using Deep Learning. In: Md. Zain, Z., Sulaiman, M.H., Mohamed, A.I., Bakar, M.S., Ramli, M.S. (eds) Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol 842. Springer, Singapore. https://doi.org/10.1007/978-981-16-8690-0_80
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DOI: https://doi.org/10.1007/978-981-16-8690-0_80
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