Buckle Pose Estimation Using a Generative Adversarial Network
Source abstract
Abstract
The buckle before the lens coating is still typically disassembled manually. The difference between the buckle and the background is small, while that between the buckles is large. This mechanical disassembly can also damage the lens. Therefore, it is important to estimate pose with high accuracy. This paper proposes a buckle pose estimation method based on a generative adversarial network. An edge extraction model is designed based on a segmentation network as the generator. Spatial attention is added to the discriminator to help it better distinguish between generated and real graphs. The generator thus generates delicate external contours and center edge lines with help from the discriminator. The external rectangle and the least square methods are used to determine the center position and deflection angle of the buckle, respectively. The center point and angle accuracies of the test datasets are 99.5% and 99.3%, respectively. The pixel error of the center point distance and the absolute error of the angle to the horizontal line are within 7.36 pixels and 1.98°, respectively. This method achieves the highest center point and angle accuracies compared to Hed, RCF, DexiNed, and PidiNet. It can meet practical requirements and boost the production efficiency of lens coatings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image and Object Detection Techniques · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical Engineering Computer Science, Ningbo University, Ningbo 315211, China
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Funding
Zhejiang Provincial Natural Science Foundation · Technology Innovation 2025 Major Project · Ningbo Medical Science and Technology Plan Project · Ningbo Science and Technology Program for the Public Interest
References (40)
Show source-provided references
1. Pipe pose estimation based on machine vision10.1016/j.measurement.2021.109585 ↗
2. Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum10.1109/TII.2014.2359416 ↗
3. Generative Adversarial Nets
4. Yu, F., and Koltun, V. (2016, January 2–4). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the ICLR, San Juan, Puerto Rico.
5. Huang, G., Chen, J., and Liu, L. (2023). One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology. Appl. Sci., 13.10.3390/app13031734 ↗
6. Zhao, F., Xu, L., Lv, L., and Zhang, Y. (2022). Wheat Ear Detection Algorithm Based on Improved YOLOv4. Appl. Sci., 12.10.3390/app122312195 ↗
7. Hwang, B., Lee, S., and Han, H. (2023). DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection. Appl. Sci., 13.10.3390/app13031841 ↗
8. Viola, P., and Jones, M. (2001, January 8–14). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA.
9. Wavelets and Multiresolution Processing
10. Dalal, N., and Triggs, B. (2005, January 20–26). Histograms of Oriented Gradients for Human Detection. Proceedings of the Computer Vision and Pattern Recognition, San Diego, CA, USA.
11. Object Detection with Discriminatively Trained Part-Based Models10.1109/TPAMI.2009.167 ↗
12. Support Vector Machines10.1109/5254.708428 ↗
13. Connector Surface Crack Detection Method
14. Machine Vision Measurement Method of Tooth Pitch Based on Gear Local Image
15. Fault Detection of Train Center Plate Bolts Loss Using Modified LBP and Optimization Algorithm10.2174/1874444301507011916 ↗
16. A threshold selection method from gray-level histograms10.1109/TSMC.1979.4310076 ↗
17. Interacting with paper on the DigitalDesk10.1145/159544.159630 ↗
18. Beucher, S., and Lantuejoul, C. (1979, January 17–21). Use of Watersheds in Contour Detection. Proceedings of the International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, Rennes, France.
19. Deep learning10.1038/nature14539 ↗
20. Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., and LeCun, Y. (October, January 27). What is the best multi-stage architecture for object recognition?. Proceedings of the 12th International Conference on Computer Vision Workshops, Kyoto, Japan.
21. Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation10.1162/neco.2009.10-08-881 ↗
22. Representation Learning: A Review and New Perspectives10.1109/TPAMI.2013.50 ↗
23. PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection10.1109/TII.2019.2958826 ↗
24. Recognition Method for Spray-Painted Workpieces Based on Mask R-CNN and Fast Point Feature Histogram Feature Pairing
25. He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22–29). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
26. Research on Workpiece Recognition Method Based on Improved YOLOv3
27. Redmon, J., and Yolov, F.A. (2018). An Incremental Improvement. arXiv.
28. Detection Algorithm of Train Bottom Parts Based on OSE-dResnet Network
29. Hu, J., Shen, L., and Albanie, S. (2018, January 18–22). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
30. He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the of the 2016 Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
31. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
32. Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards Real-Time Object Detection with Region Proposal Networks. Adv. Neural Inf. Process. Syst., 28.
33. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
34. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). SSD: Single Shot Multibox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
35. Image Scene Geometry Recognition Using Low-Level Features Fusion at Multi-layer Deep CNN10.1016/j.neucom.2021.01.085 ↗
36. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of thes European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
37. Xie, S., and Tu, Z. (2015, January 11–18). Holistically Nested Edge Detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.164 ↗
38. Liu, Y., Cheng, M.M., Hu, X., Wang, K., and Bai, X. (2017, January 21–26). Richer Convolutional Features for Edge Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.622 ↗
39. Dense Extreme Inception Network for Edge Detection10.1016/j.patcog.2023.109461 ↗
40. Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., and Liu, L. (2021, January 11–17). Pixel Difference Networks for Efficient Edge Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada.10.1109/ICCV48922.2021.00507 ↗