Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
Source abstract
Abstract
Space-time image velocimetry (STIV) plays an important role in river velocity measurement due to its safety and efficiency. However, its practical application is affected by complex scene conditions, resulting in significant errors in the accurate estimation of texture angles. This paper proposes a method to predict the texture angles in frequency domain images based on an improved ShuffleNetV2. The second 1 × 1 convolution in the main branch of the downsampling unit and basic unit is deleted, the kernel size of the depthwise separable convolution is adjusted, and a Bottleneck Attention Module (BAM) is introduced to enhance the ability of capturing important feature information, effectively improving the precision of texture angles. In addition, the measured data from a current meter are used as the standard for comparison with established and novel approaches, and this study further validates its methodology through comparative experiments conducted in both artificial and natural river channels. The experimental results at the Agu, Panxi, and Mengxing hydrological stations demonstrate that the relative errors of the discharge measured by the proposed method are 2.20%, 3.40%, and 2.37%, and the relative errors of the mean velocity are 1.47%, 3.64%, and 1.87%, which affirms it has higher measurement accuracy and stability compared with other methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrology and Sediment Transport Processes · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, China
Yuxi Branch of Yunnan Hydrology and Water Resources Bureau, Yuxi 653100, China
Lincang Branch of Yunnan Hydrology and Water Resources Bureau, Lincang 677000, China
Funding
National Natural Science Foundation of China · Yunnan Xingdian Talents Support Plan
References (32)
Show source-provided references
1. Ju, Z. (2018). Research on River Velocity Measurement Based on Video and Image Recognition. [Master’s Thesis, Zhejiang University of Technology].
2. Assessment of wide river characteristics using an acoustic Doppler current profiler10.1061/(ASCE)HE.1943-5584.0001447 ↗
3. Advances of Non-contact Instruments and Techniques for Open-channel Flow Measurements
4. Review of image-based river surface velocimetry research
5. Scattering particle characteristics and their effect on pulsed laser measurements of fluid flow: Speckle velocimetry vs particle image velocimetry10.1364/AO.23.001690 ↗
6. Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications10.1080/00221689809498626 ↗
7. Optical flow for image-based river velocity estimation10.1016/j.flowmeasinst.2018.11.009 ↗
8. Fujita, I., and Tsubaki, R. (August, January 28). A Novel Free-Surface Velocity Measurement Method Using Spatio-Temporal Images. Proceedings of the 2002 Hydraulic Measurements and Experimental Method Specialty Conference, Estes Park, CO, USA.10.1061/40655(2002)85 ↗
9. Development of a non-intrusive and efficient flow monitoring technique: The space-time image Velocimetry (STIV)10.1080/15715124.2007.9635310 ↗
10. Efficient and accurate estimation of water surface velocity in STIV10.1007/s10652-018-9651-3 ↗
11. Zhen, Z., Huabao, L., Yang, Z., and Jian, H. (2019, January 1–3). Design and evaluation of an FFT-based space-time image velocimetry (STIV) for time-averaged velocity measurement. Proceedings of the 2019 14th IEEE International Conference on Electronic Measurement & Instruments, Changsha, China.10.1109/ICEMI46757.2019.9101763 ↗
12. An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge10.1016/j.flowmeasinst.2020.101864 ↗
13. Lu, J., Yang, X., and Wang, J. (2023). Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV. Sensors, 23.10.3390/s23020955 ↗
14. River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics10.1088/1361-6501/ad232c ↗
15. Watanabe, K., Fujita, I., Iguchi, M., and Hasegawa, M. (2021). Improving Accuracy and Robustness of Space-Time Image Velocimetry (STIV) with Deep Learning. Water, 13.10.3390/w13152079 ↗
16. Surface space-time image velocimetry of river based on residual network
17. Optimization of Space-Time image velocimetry based on deep residual learning10.1016/j.measurement.2024.114688 ↗
18. Sensitivity analysis of image filter for space-time image velocimetry in frequency domain
19. Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8–14). ShuffleNetV2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.10.1007/978-3-030-01264-9_8 ↗
20. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
21. Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018). Bam: Bottleneck Attention Module. arXiv.
22. (2015). Code for Liquid Flow Measurement in Open Channels (Standard No. GB 50179-2015). (In Chinese).
23. 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 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
24. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
25. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). 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 ↗
26. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
27. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
28. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
29. Tan, M., and Le, Q. (2021, January 18–24). Efficientnetv2: Smaller models and faster training. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual.
30. 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 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
31. Image Flow Measurement Based on the Combination of Frame Difference and Fast and Dense Optical Flow
32. Kroeger, T., Timofte, R., Dai, D., and Van Gool, L. (2016, January 11–14). Fast optical flow using dense inverse search. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46493-0_29 ↗