Space-Time Image Velocimetry Based on Improved MobileNetV2

Abstract

Space-time image velocimetry (STIV) technology has achieved good performance in river surface-flow velocity measurement, but the application in a field environment is affected by bad weather or lighting conditions, which causes large measurement errors. To improve the measurement accuracy and robustness of STIV, we combined STIV with deep learning. Additionally, considering the light weight of the neural network model, we adopted MobileNetV2 and improved its classification accuracy. We name this method MobileNet-STIV. We also constructed a sample-enhanced mixed dataset for the first time, with 180 classes of images and 100 images per class to train our model, which resulted in a good performance. Compared to the current meter measurement results, the absolute error of the mean velocity was 0.02, the absolute error of the flow discharge was 1.71, the relative error of the mean velocity was 1.27%, and the relative error of the flow discharge was 1.15% in the comparative experiment. In the generalization performance experiment, the absolute error of the mean velocity was 0.03, the absolute error of the flow discharge was 0.27, the relative error of the mean velocity was 6.38%, and the relative error of the flow discharge was 5.92%. The results of both experiments demonstrate that our method is more accurate than the conventional STIV and large-scale particle image velocimetry (LSPIV).

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

“…To verify the accuracy of the algorithm in measuring natural rivers in complex environments, algorithm comparison tests were conducted on Baoji Hydrological Station in Qianxinan Prefecture, Guizhou Province, and Agu Hydrological Station in Yimen County, Yuxi City, Yunnan Province was selected for a regular river channel universality test. In each experiment, the results of the algorithm proposed in this paper were compared and analyzed with the measurement outcomes of other algorithms, including flow velocity meter, LSPIV, STIV, MobileNet-STIV[27] and FD-DIS-G[3], concerning hydrological parameters like vertical average velocity, mean velocity, and total discharge. In the comparison algorithm, MobileNet-STIV is an algorithm that transfers ST images from the space time domain to the frequency domain, and uses the improved MobileNet V2+ for angle detection in the frequency domain.…”