심층신경망을 이용한 PCB 부품의 검지 및 인식

Detection of PCB Components Using Deep Neural Nets

  • 조태훈 (한국기술교육대학교 컴퓨터공학부)
  • Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
  • 투고 : 2020.04.20
  • 심사 : 2020.06.11
  • 발행 : 2020.06.30

초록

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

키워드

참고문헌

  1. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791
  2. A. Krizhevsky, I. Sutskever, G.E. Hinton, "ImageNet classification with deep convolutional neural networks," Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, pp. 1097-1105, Dec. 2012.
  3. K. He, X. Zhang. S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proceeding of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
  4. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in Proceeding of the International Conference on Learning Representations (ICLR), 2015.
  5. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, June 2017. https://doi.org/10.1109/TPAMI.2016.2577031
  6. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the 2016 IEEE International Conference on Computer Vision, pp. 779-788, 2016.
  7. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed, "SSD: Single shot multibox detector," in Proceedings of ECCV, 2016.
  8. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proceeding of the 2017 IEEE International Conference on Computer Vision, Venice, Italy, pp. 2999-3007, 2017.
  9. Y.-H. Lee and Y. Kim, "Comparison of CNN and YOLO for Object Detection," Journal of the Semiconductor and Display Technology, vol.19, no.1, pp. 85-92, 2020.
  10. L. Ale, N. Zhang, and L. Li, "Road damage detection using RetinaNet," in Proceedings of 2018 IEEE International Conference on Big Data (Big Data), pp. 5197-5200, 2018.
  11. T.M. Hoang, P.H. Nguyen, N.Q. Truong, Y.W. Lee and K.R. Park, "Deep RetinaNet-based detection and classification of road markings by visible light camera sensors," Sensors, vol.19, no.2, 2019. https://doi.org/10.3390/s19020277
  12. Y. Wang, C. Wang, H. Zhang, Y. Dong, and S. Wei, "Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery," Remote Sensing, vol.11, no.5, 2019.
  13. M. Zlocha, Q. Dou, and B. Glocker, "Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels," in Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct. 2019.
  14. T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceeding of the 2017 IEEE International Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 936-944, 2017.
  15. LabelImg : a graphical image annotation tool and label object bounding boxes in images [Internet]. Available: https://github.com/tzutalin/labelImg.
  16. Keras implementation of RetinaNet object detection [Internet]. Available: https://github.com/fizyr/kerasretinanet.
  17. E. Mark, L.V. Gool, C.K.I. Williams, J. Winn, and A. Zisserman, "The PASCAL Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, pp. 303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4