TY - GEN
T1 - Detection of Micro-defects on Metal Screw Surfaces Based on Faster Region-Based Convolutional Neural Network
AU - Patar, Mohd Nor Azmi Ab
AU - Ayub, Muhammad Azmi
AU - Zainal, Nur Aainaa
AU - Rosly, Muhammad Aliff
AU - Lee, Hokyoo
AU - Hanafusa, Akihiko
N1 - Funding Information:
Acknowledgements The author would like to express sincere gratitude to the supervisor for the guidance in completing this project. The authors also gratefully acknowledge the Ministry of Education Malaysia (MOE) for the fund received through the Fundamental Research Grant Scheme (FRGS) [Project file: 600-IRMI/FRGS 5/3 (472/2019)].
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The detection of defects in a product is one of required production process for quality control. Currently, the quality control process of metal screws uses many manpower for manual inspection. Hence, this study about to implement faster region-based convolutional neural network (faster R-CNN) to detect the micro-defects on metal screw surfaces. The defects of surface damage, stripped screw, and dirty surface screw considered in this research. Raspberry Pi 3 with a camera module is used for image acquisition of the metal screws in determining various kinds of defects. The image is also acquired to be used for the training of the faster R-CNN. A testing is carried out to test the performance of the model. The experiment outcome shows that the detection accuracy of the model is 98.8%. The model also shows superiority in this project detection method compared with the traditional template-matching method and single-shot detector (SSD) model.
AB - The detection of defects in a product is one of required production process for quality control. Currently, the quality control process of metal screws uses many manpower for manual inspection. Hence, this study about to implement faster region-based convolutional neural network (faster R-CNN) to detect the micro-defects on metal screw surfaces. The defects of surface damage, stripped screw, and dirty surface screw considered in this research. Raspberry Pi 3 with a camera module is used for image acquisition of the metal screws in determining various kinds of defects. The image is also acquired to be used for the training of the faster R-CNN. A testing is carried out to test the performance of the model. The experiment outcome shows that the detection accuracy of the model is 98.8%. The model also shows superiority in this project detection method compared with the traditional template-matching method and single-shot detector (SSD) model.
KW - Faster region-based convolutional neural network
KW - Single-shot detector
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U2 - 10.1007/978-981-16-6482-3_58
DO - 10.1007/978-981-16-6482-3_58
M3 - Conference contribution
AN - SCOPUS:85121811038
SN - 9789811664816
T3 - Smart Innovation, Systems and Technologies
SP - 587
EP - 597
BT - Intelligent Manufacturing and Energy Sustainability - Proceedings of ICIMES 2021
A2 - Reddy, A. N.
A2 - Marla, Deepak
A2 - Favorskaya, Margarita N.
A2 - Satapathy, Suresh Chandra
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2021
Y2 - 18 June 2021 through 19 June 2021
ER -