A Multiorder Attentional Spatial Interactive Convolutional Neural Network (MoAS-CNN) for Low-Resolution Haptic Recognition
Source abstract
Abstract
In haptic recognition, pressure information is usually represented as an image, and then used for feature extraction and classification. Deep learning that processes haptic information in end-to-end manner has attracted attention. This study proposes a multiorder attentional spatial interactive convolutional neural network (MoAS-CNN) for haptic recognition. The asymmetric dual-stream all convolutional neural network with integrated channel attention module is applied for automatic first-order feature extraction. Later on, the spatial interactive features based on the overall feature map are computed to improve the second-order description capability. Finally, the multiorder features are summed to improve the feature utilization efficiency. To validate the MoAS-CNN, we construct a haptic acquisition platform based on three-scale pressure arrays and collect haptic letter-shape (A–Z) datasets with complex contours. The recognition accuracies are 95.73% for 16 × 16, 98.37% for 20 × 20 and 98.65% for 32 × 32, which significantly exceeds the traditional first- and second-order CNNs and local SIFT feature.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tactile and Sensory Interactions · Teleoperation and Haptic Systems · Gaze Tracking and Assistive Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Microelectronics, Xidian University, Xi’an 710071, China
Suzhou Honghu Qiji Electronic Technology Co., Ltd., Suzhou 215008, China
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
Funding
National Natural Science Foundation of China · Natural Science Basic Research Plan in Shaanxi Province of China · Wuhu-Xidian University Industry-University-Research Cooperation Special Fund
References (29)
Show source-provided references
1. A Review of Tactile Information: Perception and Action through Touch10.1109/TRO.2020.3003230 ↗
2. Smart Design of Surgical Suture Attachment Force Measurement Setup Using Tactile Sensor10.1109/TIM.2020.3031757 ↗
3. Robotic Tactile Perception of Object Properties: A Review10.1016/j.mechatronics.2017.11.002 ↗
4. Novel Tactile-SIFT Descriptor for Object Shape Recognition10.1109/JSEN.2015.2432127 ↗
5. Pohtongkam, S., and Srinonchat., J. (2022, January 9–11). Object Recognition Using Glove Tactile Sensor. Proceedings of the 2022 International Electrical Engineering Congress (iEECON), Khon Kaen, Thailand.10.1109/iEECON53204.2022.9741672 ↗
6. Deep Learning for Computer Vision: A Brief Review10.1155/2018/7068349 ↗
7. Deep visual tracking: Review and experimental comparison10.1016/j.patcog.2017.11.007 ↗
8. CNN-Based Methods for Object Recognition with High-Resolution Tactile Sensors10.1109/JSEN.2019.2912968 ↗
9. Convolutional Autoencoder for feature extraction in tactile sensing10.1109/LRA.2019.2927950 ↗
10. End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble10.1007/s12559-018-9568-7 ↗
11. Skin electronics from scalable fabrication of an intrinsically stretchable transistor array10.1038/nature25494 ↗
12. Bionic Compound Eye-Inspired High Spatial and Sensitive Tactile Sensor10.1109/TIM.2021.3063757 ↗
13. Brahimi, S., Aoun, N.B., and Amar, C.B. (2018, January 7–10). Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.10.1109/SMC.2018.00428 ↗
14. Touchpoint-tailored ultra-sensitive piezoresistive pressure sensors with a broad dynamic response range and low detection limit10.1021/acsami.8b20284 ↗
15. Ruderman, A., Rabinowitz, N.C., Morcos, A.S., and Zoran, D. (2018). Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs. arXiv.
16. Springenber, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv.
17. Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition10.1109/TPAMI.2017.2723400 ↗
18. Carreira, J., Caseiro, R., Batista, J., and Sminchisescu, C. (2012, January 7–13). Semantic Segmentation with Second-Order Pooling. Proceedings of the 12th European conference on Computer Vision(ECCV), Florence, Italy.10.1007/978-3-642-33786-4_32 ↗
19. Akilan, T., Wu, Q., Safaei, A., and Jiang, W. (2017, January 5–8). A late fusion approach for harnessing multi-cnn model high-level features. Proceedings of the 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC), Banff, AB, Canada.10.1109/SMC.2017.8122666 ↗
20. Deep Sparse Rectifier Neural Networks
21. Squeeze-and-excitation networks10.1109/TPAMI.2019.2913372 ↗
22. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lile, France.
23. Understanding the difficulty of training deep feedforward neural networks
24. Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.
25. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
26. (2022, November 11). The CIFAR-10 Dataset. Available online: https://www.cs.toronto.edu/~kriz/cifar.html.
27. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., and Unterthiner, T. (2021, January 20–27). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the 2021 International Conference on Learning Representations (ICLP), Colombo, Sri Lanka.
28. Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H. (2021, January 10–17). Going deeper with Image Transformers. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.00010 ↗
29. Tan, M., and Le, Q.V. (2019, January 9–15). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 2019 International Conference on Machine Learning (ICML), Long Beach, CA, USA.