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View all- Kong WTan ZFan WTao XWang MXu LXu X(2024)A review of the application of staircase scene recognition system in assisted motionDigital Signal Processing10.1016/j.dsp.2023.104362146:COnline publication date: 25-Jun-2024
Object Detection Algorithm of Stairs Based on Multi-information Fusion
Authors: 
Weifeng Kong, Zhiying Tan, Xu Tao, Wenbo Fan, Yan Ji, Meiling Wang, Xiaobin XuAuthors Info & Claims
Weifeng Kong, Zhiying Tan, Xu Tao, Wenbo Fan, Yan Ji, Meiling Wang, Xiaobin XuAuthors Info & ClaimsPages 215 - 221
Abstract
In view of the mobile robot's perception of the environment in the process of moving, the method of deep learning is used to detect the target terrain in order to send follow-up instructions to the robot. In this paper, a lightweight stairs detection algorithm based on improved YOLOv5-Lite is proposed, which uses RepVGG as the backbone network to extract features to build YOLOv5-Lite lightweight neural network model, and removes the Focus layer in the backbone network and replaces it with a 6x6 convolution layer to reduce the number of network parameters to improve the speed of model detection. Through the introduction of BasicRFB_s pooling layer, increase the receptive field of the network, and add C3SE attention mechanism in the pooling layer to reduce pooling loss. To decrease the mistakes, we also segment the point cloud of stairs into vertical and horizontal planes by the difference of normal and consider their probability distribution for the further detection. The experimental results show that the accuracy of the improved lightweight algorithm can reach 97.1%, and the frame rate can reach 51 fps. After segmenting the point cloud, the accuracy of detection method can reach 99.9% and the frame rate can reach 46 fps. The results show that the proposed method has the characteristics of real-time, lightweight and high accuracy.
References
[1]
Shuihua Wang, Hangrong Pan, Chenyang Zhang and Yingli Tian. 2013. RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. Journal of Visual Communication and Image Representation. 25,2 (November 2013): 263-272. http://dx.doi.org/10.1016/j.jvcir.2013.11.005
[2]
Rai Munoz, Xuejian Rong and Yingli Tian. 2016. Depth-aware indoor staircase detection and recognition for the visually impaired. In Proceedings of IEEE international conference on multimedia & expo workshops (ICMEW’16). IEEE, Seattle, WA, USA, 1-6. http://doi.org/10.1109/ICMEW.2016.7574706
[3]
Perez-Yus, D. Gutierrez-Gomez, G. Lopez-Nicolas and J.J. Guerrero. 2017. Stairs detection with odometry-aided traversal from a wearable RGB-D camera. Computer Vision and Image Understanding. Computer Vision and Image Understanding. 154 ( May 2017):192-205.
[4]
https://doi.org/10.1016/j.cviu.2016.04.007
[5]
Habib Mohamad, Sadjaad Ozgoli. 2022. Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance. Robotics and Autonomous Systems. 160( November 2022): 104319. https://doi.org/10.1016/j.robot.2022.104319
[6]
J. Young and D. P. Ferris. 2017. State of the Art and Future Directions for Lower Limb Robotic Exoskeletons. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25,2 (February 2017):171-182. https://doi.org/10.1109/TNSRE.2016.2521160.
[7]
B. Laschowski, W. McNally, A. Wong and J. McPhee. 2021. Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons. In Proceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC’21). IEEE, Mexico, 4631-4635. https://doi.org/10.1109/EMBC46164.2021.9630064
[8]
B. Kleiner, N. Ziegenspeck, R. Stolyarov, H. Herr, U. 2018. Schneider and A. Verl. A Radar-Based Terrain Mapping Approach for Stair Detection Towards Enhanced Prosthetic Foot Control. In Proceedings of 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob’18). IEEE, Enschede, Netherlands, 105-110.https://doi.org/10.1109/BIOROB.2018.8487722
[9]
Hongwen Yan, Junjie Wan, Zhimin Pan, Jianjun Zhang and Rui Ma. 2022. Defect Identification of Distribution Components Based on Improved Yolov5-Lite Light-weight. High Voltage Engineering. ( August 2022):1-10. https://doi.org/10.13336/j.1003-6520.hve.20220387
[10]
X. Ding, X. Zhang, N. Ma, J. Han, G. Ding and J. Sun. 2021. RepVGG: Making VGG-style ConvNets Great Again. In Proceedings of Computer Vision and Pattern Recognition (CVPR’21). IEEE, Nashville, TN, USA, 13728-13737. https://doi.org/10.1109/CVPR46437.2021.01352
[11]
Liu, Songtao, and Di Huang. 2018. Receptive field block net for accurate and fast object detection. In Proceedings of the European Conference on Computer Vision (ECCV’18). Springer, Munich, Germany, 404-419. https://doi.org/10.1007/978-3-030-01252-6_24
[12]
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke and Alex Alemi: 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the 31-st AAAI Conference on Artificial Intelligence (AAAI’17). ACM, San Francisco,CA, USA,4278-4284. https://doi.org/10.48550/arXiv.1602.07261
[13]
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna. 2016. Rethinking the Inception Architecture for Computer Vision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE, Las Vegas, NV, USA, 2818-2826. https://doi.org/10.1109/CVPR.2016.308
[14]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich. 2015.Going deeper with convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). IEEE, Boston, MA, USA, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
[15]
J. Hu, L. Shen and G. Sun. 2018. Squeeze-and-Excitation Networks. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR;18). IEEE, Salt Lake City, UT, USA, 7132-7141. https://doi.org/10.1109/CVPR.2018.00745
[16]
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu and Alexander C. Berg. 2016. SSD: Single Shot MultiBox Detector. In Proceedings of the European Conference on Computer Vision (ECCV’16). Springer, Amsterdam, Netherlands,21-37. https://doi.org/10.1007/978-3-319-46448-0_2
[17]
S. Ren, K. He, R. Girshick and J. Sun. 2017. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence,39,6( June 2017):1137-1149, https://doi.org/10.1109/TPAMI.2016.2577031
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- Object Detection Algorithm of Stairs Based on Multi-information Fusion
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Published: 18 July 2023
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- Refereed limited
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- the Key R&D plan of Jiangsu Province
- Anhui Provincial Natural Science Foundation
- the Fundamental Research Funds for the Central Universities
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RobCE 2023
RobCE 2023: 2023 3rd International Conference on Robotics and Control Engineering
May 12 - 14, 2023
Nanjing, China
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View all- Kong WTan ZFan WTao XWang MXu LXu X(2024)A review of the application of staircase scene recognition system in assisted motionDigital Signal Processing10.1016/j.dsp.2023.104362146:COnline publication date: 25-Jun-2024
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