$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

SSD PCB Component Detection Using YOLOv5 Model 원문보기

Journal of information and communication convergence engineering, v.21 no.1, 2023년, pp.24 - 31  

Ziyu, Fang (Department of Computer Software Engineering, Silla University) ,  Pyeoungkee, Kim (Department of Computer Software Engineering, Silla University) ,  Xiaorui, Huang (Department of Computer Software Engineering, Silla University)

Abstract AI-Helper 아이콘AI-Helper

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual dete...

주제어

참고문헌 (35)

  1. V. Kasavajhala, "Solid state drive vs. hard disk drive price and performance study," Proc. Dell Tech. White Paper (2011), pp. 8-9, May. 2011. [Internet] Available: https://www.profesorweb.es/wp-content/uploads/2012/11/ssd_vs_hdd_price_and_performance_study.pdf. 

  2. K. Vatto, The truth about SSD data retention. [Internet] Available: https://www.anandtech.com/show/9248/the-truth-about-ssd-dataretention 

  3. P. Hernandez, SSDs sales rise, prices drop below $1 per GB in 2012. Jan. 2012. [Online] Available: https://www.ecoinsite.com/2012/01/ssd-salesprice-1-dollar-per-gb-2012.html. 

  4. S. Downing, Best SSDs 2022: From Budget SATA to Blazing-Fast NVMe. [Online] Available: https://www.tomshardware.com/reviews/best-ssds,3891.html. 

  5. B. Schroeder, R. Lagisetty, and A. Merchant, "Flash reliability in production: The expected and the unexpected," in 14th USENIX Conference on File and Storage Technologies (FAST 16), Santa Clara, USA, pp. 67-80, 2016. [Online]. Available: https://www.usenix.org/conference/fast16/technical-sessions/presentation/schroeder 

  6. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004. DOI: 10.1023/B:VISI.0000029664.99615.94. 

  7. H. Bay, T. Tuytelaars, and L. V. Gool, "Surf: Speeded up robust features," in European conference on computer vision, Springer, Berlin, Heidelberg, vol 3951, pp. 404-417, 2006. DOI: 10.1007/11744023_32. 

  8. A. I. M. Hassanin, F. E. Abd El-Samie, and E. B. Ghada, "A realtime approach for automatic defect detection from PCBs based on SURF features and morphological operations," Multimedia Tools and Applications, vol. 78, no. 24, pp. 34437-34457, Oct. 2019. DOI: 10.1007/s11042-019-08097-9. 

  9. S. U. Rehman, K. F. Thang, and N. S. Lai, "Automated PCB identification and defect-detection system (APIDS)," International Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 297-306, Feb. 2019. DOI: 10.11591/ijece.v9i1.pp 297-306. 

  10. A. Salunke Purva, N. Sherkar Shubhangi, and C. S. Arya, "PCB (printed circuit board) fault detection using machine learning," International Journal of Computer Science and Mobile Computing, vol. 10, no. 2, pp. 54-56, Feb. 2021. DOI: 10.47760/ijcsmc.2021.v10i02.008. 

  11. J. Fang, L. Shang, G. Gao, K. Xiong, and C. Zhang, "Capacitor detection on PCB using AdaBoost classifier," Journal of Physics: Conference Series, vol. 1631, no. 1, pp. 012185, IOP Publishing, Jul. 2020. DOI: 10.1088/1742-6596/1631/1/012185. 

  12. CW. Kuo, J. D. Ashmore, D. Huggins, and Z. Kira, "Data-efficient graph embedding learning for PCB component detection," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, USA, pp. 551-560, 2019. DOI: 10.1109/WACV.2019.00064. 

  13. J. Redmon and A. Farhadi, "YOLOv3: An incremental improvement," arXiv preprint arXiv:1804.02767, Apr. 2018. DOI: 10.48550/arXiv.1804.02767. 

  14. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 779-788, 2016. DOI: 10.1109/CVPR.2016.91. 

  15. J. Redmon, and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 6517-6525, 2017. DOI: 10.1109/CVPR.2017.690. 

  16. A. Bochkovskiy, CY. Wang, and HY. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, Apr. 2020. DOI: 10.48550/arXiv.2004.10934. 

  17. G. Jocher, Code. [Online] Available: https://github.com/ultralytics/yolov5 

  18. M. A. Reza, Z. Chen, and D. J. Crandall, "Deep neural networkbased detection and verification of microelectronic images," Journal of Hardware and Systems Security, vol. 4, no. 1, pp. 44-54, Jan. 2020. DOI: 10.1007/s41635-019-00088-4 

  19. Y. Kang and X. Li, "A novel tiny object recognition algorithm based on unit statistical curvature feature," in European Conference on Computer Vision, Amsterdam, The Netherlands, vol 9909, pp. 762-777, 2016. DOI: 10.1007/978-3-319-46454-1_46. 

  20. J. Wang, C. Xu, W. yang, and L. Yu, "A normalized gaussian Wasserstein distance for tiny object detection," arXiv preprint arXiv:2110.13389, Oct. 2021. DOI: 10.48550/arXiv.2110.13389. 

  21. J. Wang, W. Yang, H. Guo, R. Zhang, and GS. Xia, "Tiny object detection in aerial images," in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, pp. 3791-3798, 2021. DOI: 10.1109/ICPR48806.2021.9413340. 

  22. H. Rezatofighi, N. Tsoi, JY. Gwak, A. Sadeghian, I. Reid, and S. Savarese, "Generalized intersection over union: A metric and a loss for bounding box regression," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 658-666, 2019. DOI: 10.1109/CVPR.2019.00075. 

  23. P. Adarsh, P. Rathi, and M. Kumar, "YOLO v3-Tiny: Object Detection and Recognition using one stage improved model," in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 687-694, 2020. DOI: 10.1109/ICACCS48705.2020.9074315. 

  24. B. Hu and J. Wang, "Detection of PCB surface defects with improved faster-RCNN and feature pyramid network," IEEE Access, vol. 8, pp. 108335-108345, Jun. 2020. DOI: 10.1109/ACCESS.2020.3001349. 

  25. TY. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 936-944, 2017. DOI: 10.1109/CVPR.2017.106. 

  26. Y. Gong, X. Yu, Y. Ding, X. Peng, J. Zhao, and Z. Han. "Effective fusion factor in FPN for tiny object detection," in Proceedings of the IEEE/CVF winter conference on applications of computer vision, Waikoloa, USA, pp. 1160-1168, 2021. DOI: 10.1109/WACV48630.2021.00120. 

  27. S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, "Path aggregation network for instance segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 8759-8768, 2018. DOI: 10.1109/CVPR.2018.00913. 

  28. K. He, X. Zhang, S. Ren, and J. Sun. "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904-1916, 2015. DOI: 10.1109/TPAMI.2015.2389824. 

  29. W. Shi, Z. Lu, W. Wu, and H. Liu, "Single-shot detector with enriched semantics for PCB tiny defect detection," The Journal of Engineering, vol. 2020, no. 13, pp. 366-372, 2020. DOI: 10.1049/joe.2019.1180. 

  30. D. Li, L. Xu, G. Ran, and Z. Guo, "Computer vision based research on PCB recognition using SSD neural network," Journal of Physics: Conference Series, vol. 1815, no. 1, pp. 012005, IOP Publishing, 2021. DOI: 10.1088/1742-6596/1815/1/012005. 

  31. L. K. Cheong, S. A. Suandi, and S. Rahman, "Defects and components recognition in printed circuit boards using convolutional neural network," in 10th International Conference on Robotics, Vision, Signal Processing and Power Applications, Springer, Singapore, pp. 75-81, 2019. DOI: 10.1007/978-981-13-6447-1_10. 

  32. G. Mahalingam, K. M. Gay, and K. Ricanek, "PCB-metal: A PCB image dataset for advanced computer vision machine learning component analysis," in 2019 16th International Conference on Machine Vision Applications (MVA), Tokyo, Japan, pp. 1-5, 2019. DOI: 10.23919/MVA.2019.8757928. 

  33. TY. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, 2017. DOI: 10.1109/TPAMI.2018.2858826. 

  34. Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, "Yolox: Exceeding yolo series in 2021," arXiv preprint arXiv:2107.08430, Jul. 2021. DOI: 10.48550/arXiv.2107.08430. 

  35. CY. Wang, HY. M. Liao, YH. Wu, PY. Chen, JW. Hsieh, and IH. Yeh. "CSPNet: A new backbone that can enhance learning capability of CNN," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390-391, 2020. DOI: 10.1109/CVPRW50498.2020.00203. 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로