Visual Measurement of Grinding Surface Roughness Based on GE-MobileNet
Source abstract
Abstract
Grinding surface texture is random and feature information is weak, so it is difficult to extract effective features by deep learning network. In addition, the existing deep learning methods mostly adopt a large parameter model in grinding surface roughness recognition task, and the cost of deployment in embedded end is high. In order to solve these problems, a new lightweight network model GE-MobileNet (Ghost-ECA-MobileNetV3) is proposed. Based on MobileNetV3, a feature extractor is introduced into the shallow network part of the model to enhance the ability of the network to extract and suppress the surface texture feature and noise. At the same time, SE (Squeeze-and-Excitation) attention mechanism is replaced with ECA (Efficient Channel) attention mechanism with stronger performance. Finally, the deep network layer is removed to reduce the model size. The experimental results show that the accuracy of GE-MobileNet-based grinding surface roughness measurement model on test set is 94.97%, which is better than other networks. This study proves the effectiveness of the roughness measurement method based on GE-MobileNet.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Surface Roughness and Optical Measurements · Advanced machining processes and optimization · Advanced Surface Polishing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China
Guangxi Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin 541004, China
Funding
National Natural Science Foundation of China
References (34)
Show source-provided references
1. Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques10.1016/j.measurement.2016.04.039 ↗
2. A review of surface roughness generation in ultra-precision machining10.1016/j.ijmachtools.2015.02.001 ↗
3. On the use of surface roughness parameters10.1007/s11249-023-01700-z ↗
4. Whitehouse, D.J. (2002). Handbook of Surface and Nanometrology, Taylor & Francis.10.1201/9781420034196 ↗
5. Roughness parameters10.1016/S0924-0136(02)00060-2 ↗
6. Noncontact roughness measurement of turned parts using machine vision10.1007/s00170-009-2101-0 ↗
7. Nondestructive, fast, and cost-effective image processing method for roughness measurement of randomly rough metallic surfaces10.1364/JOSAA.35.000998 ↗
8. White-light interferometer without mechanical scanning10.1016/j.optlaseng.2019.105800 ↗
9. Wang, H., Wei, C., Tian, A., Liu, B., and Zhu, X. (2021, January 14–17). Surface roughness detection method of optical elements based on region scattering. Proceedings of the 10th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced and Extreme Micro-Nano Manufacturing Technologies, Chengdu, China.10.1117/12.2605355 ↗
10. The development of an ANN surface roughness prediction system of multiple materials in CNC turning10.1007/s00170-022-10709-y ↗
11. Measuring grinding surface roughness based on the sharpness evaluation of colour images10.1088/0957-0233/27/2/025404 ↗
12. Measuring grinding surface roughness based on singular value entropy of quaternion10.1088/1361-6501/ab9aa9 ↗
13. Wang, Y.H., Lai, J.Y., Lo, Y.C., Shih, C.H., and Lin, P.C. (2022). An Image-Based Data-Driven Model for Texture Inspection of Ground Workpieces. Sensors, 22.10.3390/s22145192 ↗
14. Intelligent surface roughness measurement using deep learning and computer vision: A promising approach for manufacturing quality control10.1007/s00170-023-12457-z ↗
15. Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN10.1088/1361-6501/ad1804 ↗
16. A grinding surface roughness class recognition combining red and green information10.24425/mms.2023.147959 ↗
17. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for MobileNetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
18. GhostNetv2: Enhance cheap operation with long-range attention
19. Wadekar, S.N., and Chaurasia, A. (2022). Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features. arXiv.
20. Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., and Liu, Z. (2022, January 19–24). Mobile-former: Bridging MobileNet and transformer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.00520 ↗
21. Changeable environment visual detection of grinding surface roughness based on lightweight network10.1080/10589759.2024.2341182 ↗
22. Fine-grained image analysis with deep learning: A survey10.1109/TPAMI.2021.3126648 ↗
23. A survey on deep learning-based fine-grained object classification and semantic segmentation10.1007/s11633-017-1053-3 ↗
24. Zhao, Y., Yan, K., Huang, F., and Li, J. (2021, January 19–25). Graph-based high-order relation discovery for fine-grained recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual.10.1109/CVPR46437.2021.01483 ↗
25. Lin, T.Y., RoyChowdhury, A., and Maji, S. (2015, January 7–13). Bilinear CNN models for fine-grained visual recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.170 ↗
26. Song, J., and Yang, R. (2021, January 18–22). Feature boosting, suppression, and diversification for fine-grained visual classification. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Virtual.10.1109/IJCNN52387.2021.9534004 ↗
27. Visual measurement of grinding surface roughness based on feature fusion10.1088/1361-6501/ace543 ↗
28. Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 15–21). Mnasnet: Platform-aware neural architecture search for mobile. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00293 ↗
29. Yang, T.J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., and Adam, H. (2018, January 8–14). Netadapt: Platform-aware neural network adaptation for mobile applications. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01249-6_18 ↗
30. Hu, J., Shen, L., and Sun, G. (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 ↗
31. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual.10.1109/CVPR42600.2020.00165 ↗
32. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual.10.1109/CVPR42600.2020.01155 ↗
33. (2021). Geometrical Product Specifications (GPS)—Surface Texture: Profile—Part 1: Indication of Surface Texture (Standard No. ISO 21920-1:2021). Available online: https://www.iso.org/standard/72276.html.
34. Roughness classification detection of Swin Transformer model based on the multi-angle and convertible image environment10.1080/10589759.2023.2178651 ↗