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).