Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
Source abstract
Abstract
Target detection of electronic components on PCB (Printed circuit board) based on vision is the core technology for 3C (Computer, Communication and Consumer Electronics) manufacturing companies to achieve quality control and intelligent assembly of robots. However, the number of electronic components on PCB is large, and the shape is different. At present, the accuracy of the algorithm for detecting all electronic components is not high. This paper proposes an improved algorithm based on YOLO (you only look once) V3 (Version 3), which uses a real PCB picture and a virtual PCB picture with synthesized data as a joint training dataset, which greatly increases the recognizability of training electronic components and provides the greatest possibility for data enhancement. After analyzing the feature distribution of the five dimensionality-reduced output layers of Darknet-53 and the size distribution of the detection target, it is proposed to adjust the original three YOLO output layers to four YOLO output layers and generate 12 anchor boxes for electronic component detection. The experimental results show that the mean average precision (mAP) of the improved YOLO V3 algorithm can achieve 93.07%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Visual Attention and Saliency Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
School of Electronic Information and Electrical Engineering, Anyang Institute of Technology, Anyang 455000, China
Frontier Information Technology Institute, Zhongyuan University of Technology, Zhengzhou 450007, China
Funding
National Natural Science Foundation of China
References (28)
Show source-provided references
1. Mattela, G., Pal, C., Tripathi, M., Gavval, R., and Acharyya, A. (2019, January 7–11). Enterprise Class Deep Neural Network Architecture for recognizing objects and faces for surveillance systems. Proceedings of the 2019 11th International Conference on Communication Systems & Networks (COMSNETS), Bengaluru, India.10.1109/COMSNETS.2019.8711399 ↗
2. Tang, C., Ling, Y., Yang, X., Jin, W., and Zheng, C. (2018). Multi-view object detection based on deep learning. Appl. Sci., 8.10.3390/app8091423 ↗
3. Hyun, C., Seo, J., Lee, K.E., and Park, H. (2019). Multi-attribute recognition of facial images considering exclusive and correlated relationship among attributes. Appl. Sci., 9.10.3390/app9102034 ↗
4. Multi-vehicle detection algorithm through combining Harr and HOG features10.1016/j.matcom.2017.12.011 ↗
5. A coarse-to-fine approach for fast deformable object detection10.1016/j.patcog.2014.11.006 ↗
6. Imagenet large scale visual recognition challenge10.1007/s11263-015-0816-y ↗
7. Real-time small traffic sign detection with revised faster-RCNN10.1007/s11042-018-6428-0 ↗
8. Ni, Z.H., Sang, N., and Tan, C. (2017). Deep learning based hand gesture recognition in complex scenes. Mippr 2017: Pattern Recognition and Computer Vision, SPIE Press.10.1117/12.2284977 ↗
9. Liu, X.Y., Tian, Y.C., Yuan, C., Zhang, F.F., and Yang, G. (2018). Opium Poppy Detection Using Deep Learning. Remote Sens., 10.10.3390/rs10121886 ↗
10. Extracting PCB components based on color distribution of highlight areas10.2298/CSIS1001013Z ↗
11. The Detection of Electrical and Electronics Components using K nearest Neighbor (KNN) classification Algorithm
12. Li, W., Jiang, C., Breier, M., and Merhof, D. (2016, January 14–17). Localizing components on printed circuit boards using 2d information. Proceedings of the 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan.10.1109/ICIT.2016.7474848 ↗
13. Tang, S., He, F., Huang, X., and Yang, J. (2019). Online PCB Defect Detector on A New PCB Defect Dataset. arXiv.
14. Huang, W., and Wei, P. (2019). A PCB Dataset for Defects Detection and Classification. arXiv.
15. Kuo, C.-W., Ashmore, J., Huggins, D., and Kira, Z. (2019, January 8–10). Data-Efficient Graph Embedding Learning for PCB Component Detection. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Hilton Waikoloa Village, Hawaii, USA.10.1109/WACV.2019.00064 ↗
16. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
17. Yang, J., Li, S., Gao, Z., Wang, Z., and Liu, W. (2018). Real-Time Recognition Method for 0.8 cm Darning Needles and KR22 Bearings Based on Convolution Neural Networks and Data Increase. Appl. Sci., 8.10.3390/app8101857 ↗
18. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2019, January 16–20). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.
19. Honda, H. (2019, July 12). Reproducing Training Performance of YOLOv3 in PyTorch. Available online: https://medium.com/@hirotoschwert/reproducing-training-performance-of-yolov3-in-pytorch-part1-620140ad71d3.
20. (2019, July 12). website findchips. Available online: https://www.findchips.com.
21. (2019, July 12). Synthetic Data. Available online: https://en.wikipedia.org/wiki/Synthetic_data#cite_note-13.
22. Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., and Birchfield, S. (2019, January 16–20). Training deep networks with synthetic data: Bridging the reality gap by domain randomization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.10.1109/CVPRW.2018.00143 ↗
23. Patki, N., Wedge, R., and Veeramachaneni, K. (2016, January 17–19). The synthetic data vault. Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada.10.1109/DSAA.2016.49 ↗
24. What makes good synthetic training data for learning disparity and optical flow estimation?10.1007/s11263-018-1082-6 ↗
25. Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv.
26. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
27. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2018, January 8–14). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Munich, Germany.
28. Leo, M., Furnari, A., Medioni, G.G., Trivedi, M., and Farinella, G.M. (2018, January 8–14). Deep Learning for Assistive Computer Vision. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-11024-6_1 ↗