Comparison of bubble detectors and size distribution estimators

Comparison of bubble detectors and size distribution estimators

Článek WoS

Detection, counting and characterization of bubbles, that is, transparent objects in a liquid, is important in many industrial applications. These applications include monitoring of pulp delignification and multiphase dispersion processes common in the chemical, pharmaceutical, and food industries. Typically the aim is to measure the bubble size distribution. In this paper, we present a comprehensive comparison of bubble detection methods for challenging industrial image data. Moreover, we compare the detection-based methods to a direct bubble size distribution estimation method that does not require the detection of individual bubbles. The experiments showed that the approach based on a convolutional neural network (CNN) outperforms the other methods in detection accuracy. However, the boosting-based approaches were remarkably faster to compute. The power spectrum approach for direct bubble size distribution estimation produced accurate distributions and it is fast to compute, but it does not provide the spatial locations of the bubbles. Selecting the most suitable method depends on the specific application.

Detection, counting and characterization of bubbles, that is, transparent objects in a liquid, is important in many industrial applications. These applications include monitoring of pulp delignification and multiphase dispersion processes common in the chemical, pharmaceutical, and food industries. Typically the aim is to measure the bubble size distribution. In this paper, we present a comprehensive comparison of bubble detection methods for challenging industrial image data. Moreover, we compare the detection-based methods to a direct bubble size distribution estimation method that does not require the detection of individual bubbles. The experiments showed that the approach based on a convolutional neural network (CNN) outperforms the other methods in detection accuracy. However, the boosting-based approaches were remarkably faster to compute. The power spectrum approach for direct bubble size distribution estimation produced accurate distributions and it is fast to compute, but it does not provide the spatial locations of the bubbles. Selecting the most suitable method depends on the specific application.

Bubble detection
Size distribution estimation
Boosting-based detection
Convolutional neural networks (CNN)
Pulping

Bubble detection
Size distribution estimation
Boosting-based detection
Convolutional neural networks (CNN)
Pulping

KÄLVIÄINEN, H.; EEROLA, T.; LENSU, L.; ILONEN, J.; ZEMČÍK, P.; JURÁNKOVÁ, M.; JURÁNEK, R.

2020

01.01.2018

0167-8655

Pattern Recognition Letters

101

1

Nizozemsko

60

66

7

https://www.sciencedirect.com/science/article/pii/S0167865517304282

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