Multi – oil droplet recognition of oil-water two-phase flow based on integrated features

Abstract

Multi-oil droplet target recognition is one of the applications of machine vision in the measurement of oil-water two-phase flow parameters, which could combine other algorithms to obtain the oil droplet velocity and the water holdup of oil water two-phase flow. Appropriate target representation features can improve the recognition effect of multiple oil droplets. However, due to shooting environment differences and quality differences of oil-water two-phase flow images, existing target representation features do not perform well in low-quality oil-water two-phase flow images. To improve the precision of multi-oil droplet target recognition in oil-water two-phase flow and reduce the miss rate, this paper constructs an integrated feature on the basis of aggregate channel features (ACF). The integrated feature named aggregate channel features with histogram of local gravitational feature（ACFHG） contains the color feature channels reflecting the overall color features of the oil droplet sample, the gradient amplitude channel reflecting the overall gradient of the oil droplet sample image, the gradient direction histogram feature channels reflecting the local gradient of the oil droplet sample image, and the local gravitational feature channels that ensure oil droplet target recognition in low quality photos and photos taken in complex shooting environments. Moreover, the rotation invariance is obtained by taking the oriented gradient histogram of the local gravitational feature to further improve the multi-oil droplet target recognition effect. Experiment results show that the average precision of multi-oil droplet target recognition using the integrated features is 83.38%, which is 9.93% higher than that with using ACF, and the miss rate is 9.13%, which is 57.18% lower than that with using ACF. Compared with other existing target detection methods, the method proposed in this paper still has an advantage in the rate of missed detection.