Overlapping bubble detection and tracking method based on convolutional Neural network and Kalman Filter

Abstract

Gas-liquid bubbly flow is widely applied in chemical process engineering. Geometric and dynamic parameters of bubbles play an essential role in the numerical prediction of mass and heat transfer processes. However, the critical obstacle in bubble detection is the inability of bubble segmentation and reconstruction when the overlapping issue of multiple bubbles is serious under high void fraction conditions. A new detection and tracking technique for overlapping bubbles was proposed in this paper to identify the overlapped bubbles. First, a novel convolutional neural network is used to detect bubbles. Afterward, the relationship between the detected bubbles in two frames is correlated using the Kalman Filter and neural network. The algorithm achieves 85 % accuracy under high overlap rate conditions in a 10 mm narrow rectangular channel with around 0.1 s for an image. In addition, a comparison test was conducted to evaluate the present technique's accuracy and robustness compared with conventional methods.