An instance mask representation for bubble size distribution in two-phase bubble flotation column based on deep learning model

IF 2.3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Flow Measurement and Instrumentation Pub Date : 2025-03-19 DOI:10.1016/j.flowmeasinst.2025.102892

Zhiping Wen , Maiqiang Zhou , Sanja Mišković , Changchun Zhou

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引用次数: 0

Abstract

Measuring bubble size distribution from images sourced from various two-phase bubble systems presents a significant challenge, yet it holds substantial interest for many researchers in the field. This study introduces a novel approach by leveraging instance segmentation techniques based on deep learning to automatically quantify the bubble size distribution. The effective Mask RCNN and SOLO v2 were used as the basic model structure. The findings reveal that models employing the ResNet-FPN backbone outperform those using ResNet-C4/DC5 backbones in bubble segmentation. Specifically, the Mask RCNN with ResNet 101-FPN backbone achieved an average precision (AP) of 71.34 % for IoU = 0.50 and 68.13 % for IoU = 0.75. In terms of inference time, the SOLO v2 model displayed superior efficiency, taking 0.57 s per image compared to the Mask RCNN model. The study successfully demonstrated the utilization of the least squares fitting method to effectively detect and calculate bubble size distribution.

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来源期刊

Flow Measurement and Instrumentation 工程技术-工程：机械

CiteScore

4.30

自引率

13.60%

发文量

123

审稿时长

6 months

期刊介绍： Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions. FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest: Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible. Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems. Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories. Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.