Verification of algorithm for automatic detection of electronic devices mounted on waste printed circuit boards

References

• Adibhatla, V. A., H. C. Chih, C. C. Hsu, J. Cheng, M. F. Abbod, and J. S. Shieh. 2020. Defect detection in printed circuit boards using you-only-look-once convolutional neural networks. Electron 9:1547. doi:https://doi.org/10.3390/electronics9091547.
   Web of Science ®Google Scholar
• Adibhatla, V. A., H. C. Chih, C. C. Hsu, J. Cheng, M. F. Abbod, and J. S. Shieh. 2021. Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once. Math. Biosci. Eng. 18:4411–28. doi:https://doi.org/10.3934/mbe.2021223.
   PubMed Web of Science ®Google Scholar
• Aizawa, H., Y. Hirai, and S. Sakai. 2009. Recycling of small electrical and electronic equipment waste. J. Mater. Cycles Waste Mgt. 20:371–82. (in Japanese).
   Google Scholar
• Chiah, C. K., C. X. Teng, C. K. Siew, and S. Y. Chark. 2012. 2-d location pointing system for individual component on device under test by using LabVIEW. Proc. 2012 IEEE Conf. Sustain. Util. Dev. Engin. Tech., Kuala Lumpur, Malaysia, 248–52.
   Google Scholar
• Dai, W. T., A. Mujeeb, M. Erdt, and A. Sourin. 2020. Soldering defect detection in automatic optical inspection. Adv. Eng. Inform. 43:101004. doi:https://doi.org/10.1016/j.aei.2019.101004.
   Web of Science ®Google Scholar
• Dervisevic, I., D. Minic, Z. Kamberovic, V. Cosovic, and M. Ristic. 2013. Characterization of PCBs from computers and mobile phones, and the proposal of newly developed materials for substitution of gold, lead and arsenic. Environ. Sci. Pollut. Res. 20:4278–92. doi:https://doi.org/10.1007/s11356-012-1448-1.
   PubMed Web of Science ®Google Scholar
• Esaki, H., K. Kagii, T. Umezaki, and T. Horikoshi. 2006. Hierarchical automatic classification system of electronic parts. Trans. Inst. Elect. Engin. Japan. C 126:1447–53. (in Japanese).
   Google Scholar
• Hayashi, N., S. Koyanaka, and T. Oki. 2019. Constructing an automatic object-recognition algorithm using labeling information for efficient recycling of WEEE. Waste Manag. 88:337–46. doi:https://doi.org/10.1016/j.wasman.2019.03.065.
   PubMed Web of Science ®Google Scholar
• Hu, B., and J. H. Wang. 2020. Detection of PCB surface defects with improved Faster-RCNN and feature pyramid network. IEEE Access 8:108335–45. doi:https://doi.org/10.1109/ACCESS.2020.3001349.
   Google Scholar
• Lefkaditis, D., and G. Tsirigotis. 2010. Intelligent optical classification system for electronic components. Electron. Electr. Eng. 98:10–14.
   Google Scholar
• Li, Y. T., P. Kuo, and J. I. Guo. 2021. Automatic industry PCB board DIP process defect detection system based on deep ensemble self-adaption method. IEEE Trans. Compon. Packaging Manuf. Technol. 11:312–23. doi:https://doi.org/10.1109/TCPMT.2020.3047089.
   Web of Science ®Google Scholar
• Li, J., W. Y. Li, Y. Q. Chen, and J. A. Gu. 2021. A PCB electronic components detection network design based on effective receptive field size and anchor size matching. Comput. Intell. Neurosci. 6682710. doi:https://doi.org/10.1155/2021/6682710.
   Google Scholar
• Lian, J., L. T. Wang, T. Y. Liu, X. Ding, and Z. G. Yu. 2021. Automatic visual inspection for printed circuit board via novel Mask R-CNN in smart city applications. Sustain. Energy Technol. Assess. 44:101032.
   Web of Science ®Google Scholar
• Matsunaga, E., T. Minowa, K. Takahashi, H. Arai, and T. Sawada. 2015. Resource visualization technology for metal contents of printed circuit boards –Various electronic component detection using image processing. Proc. 26th Ann. Conf. Japan Soc. Mater. Cycles Waste Mgmt., Fukuoka, Japan, 169–70. (in Japanese).
   Google Scholar
• MineralPrices.com. 2020. Metal prices April 2020 in review. Accessed June 28, 2020. https://mailchi.mp/3a204a7c02c4/mineralpricescom-april-2020-4805568
   Google Scholar
• Mogi, T., M. Namekawa, and Y. Ueda. 2012. Detection of insertion error parts in electronic boards using HSV color format. Proc. 12th I.C. Intell. Sys. Design Apps., Kochi, India, 357–62.
   Google Scholar
• Muto, S. 1995. Mathematical statistics, 54–57. Tokyo, Japan: Asakura Publishing Co. Ltd. (in Japanese).
   Google Scholar
• Nishimura, K., M. Yanabe, M. Hashimoto, T. Araki, T. Nagatani, T. Moriyama, and S. Maeda. 2018. Proposal of electronic component visual inspection method with histogram-based local feature. J. Jpn. Soc. Precis. Eng. 84:664–70. (in Japanese). doi:https://doi.org/10.2493/jjspe.84.664.
   Google Scholar
• Nishimura, K., M. Yanabe, T. Nagatani, T. Moriyama, and S. Maeda. 2019. Proposal on improvement of defect identification performance by dimensional reduction focusing on localization of HOG feature in electronic component inspection. J. Jpn. Soc. Precis. Eng. 85:469–76. (in Japanese). doi:https://doi.org/10.2493/jjspe.85.469.
   Google Scholar
• Ohta, N. 2009. Color engineering, 45–62. Tokyo, Japan: Tokyo Denki University Press. (in Japanese).
   Google Scholar
• Oki, T. 2014. Physical separation technology to support the strategic development of urban mining –development of unused/hard-to-use resources and a future vision of resources for Japan. Synthes. Engl. Ed. 6:232–40.
   Google Scholar
• Silva, L. H. D., A. A. F. Junior, G. O. A. Azevedo, S. C. Oliveira, and B. J. T. Fernandes. 2021. Estimating recycling return of integrated circuits using computer vision on printed circuit boards. Appl. Sci. -Basel 11:2808. doi:https://doi.org/10.3390/app11062808.
   Web of Science ®Google Scholar
• Xia, S. Y., F. Wang, F. Xie, L. Huang, Q. Wang, and X. Ling. 2021. An efficient and robust target detection algorithm for identifying minor defects of printed circuit board based on PHFE and FL-RFCN. Front. Phys. 9:661091. doi:https://doi.org/10.3389/fphy.2021.661091.
   Web of Science ®Google Scholar
• Ye, R. F., C. S. Pan, P. Y. Hung, M. Chang, and K. Y. Chen. 2020. Detection and classification of printed circuit board assembly defects based on deep learning. Chi. J. Mech. Eng. 41:401–07.
   Google Scholar