Abstract
Printed Circuit Boards (PCBs) are often the subject of computer vision tasks, within the realms of object detection and classification. This paper provides a new PCB dataset (PCB-P) that has a greater diversity in the range of perspectives and rotations that the images are captured at, as well as a diverse selection of PCBs. These specifications allow for data quality to be identified based on the configuration of the captured image. This dataset is tested on a small range of popular image classification architectures, finding Inception V3 to be the best-performing. Additionally, configurations of reducing various perspectives and rotations are tested. This work finds there to be diminishing returns from increasing training image quantity.
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References
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Malin, B., Kalganova, T. (2024). Printed Circuit Boards at Perspectives: Towards Understanding Useful Data. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-99-3043-2_90
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DOI: https://doi.org/10.1007/978-981-99-3043-2_90
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