Space-time image velocimetry in blurred scenes based on BSTI-DCGAN data augmentation

Abstract

Due to the limited sample quantity and the complex data collection process of the blurred space-time image (BSTI) dataset, the deep learning-based space-time image velocimetry (STIV) results in larger errors when applied to blurry videos. To enhance the measurement accuracy, we propose the use of STIV in blurred scenes based on BSTI-deep convolutional generative adversarial network (DCGAN) data augmentation. Firstly, BSTI-DCGAN is developed based on the DCGAN. This network utilizes a bilinear interpolation-convolution module for upsampling and integrates coordinated attention and multi-concatenation attention to enhance the resemblance between generated and real images. Next, further expanding the dataset by using artificially synthesized space-time images subsequently, all space-time images are transformed into spectrograms to create a training dataset for the classification network. Finally, the primary spectral direction is detected using the classification network. The experimental results indicate that our approach effectively augments the dataset and improves the accuracy of practical measurements. Under the condition of video blur, the relative errors of the average flow velocity and discharge are 3.92% and 2.72%, respectively.