Estimation of River Velocity and Discharge Based on Video Images and Deep Learning

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.