BubCNN: Bubble detection using Faster RCNN and shape regression network

Haas, Tim; Schubert, Christian; Eickhoff, Moritz; Pfeifer, Herbert

doi:10.1016/j.ces.2019.115467

Cited by 106 publications

(40 citation statements)

References 7 publications

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“…The traditional classification of intersection-over-union (IoU) is modified in inclined NMS to be IoU between two inclined bounding boxes. The IoU calculation method is used [19].…”

Section: Non-maximum Suppressionmentioning

confidence: 99%

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

2022

IJIES

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Dysgraphia is a handwriting disorder and the classification of dysgraphia in children's handwritten images helps to identify the dysgraphia patient effectively and also prevents low self-esteem. Traditional methods of dysgraphia classification based on experts manually classify the dysgraphia that requires more cost and time. Few researches apply the machine learning and deep learning techniques for the classification of dysgraphia and existing models have the limitations of overfitting problems in the training process. In this research, the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network (NDR-R2CNN) is proposed to improve the efficiency of dysgraphia classification. The balancing parameters are introduced in the loss function to balance the class in the training and eliminate the features to reduce the overfitting problem. The collected children's handwriting data were used to evaluate the performance of the proposed NDR-R2CNN model. The proposed NDR-R2CNN model has the advantages of effective feature analysis and non-discrimination word analysis. The NDR-R2CNN model has the accuracy of 98.2 % and the SMOTE+SVM method has 90.2 % accuracy in Dysgraphia classification. The result shows that the NDR-R2CNN model has 98.2 % accuracy and the existing CNN has the accuracy of 94.2 %.

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“…The traditional classification of intersection-over-union (IoU) is modified in inclined NMS to be IoU between two inclined bounding boxes. The IoU calculation method is used [19].…”

Section: Non-maximum Suppressionmentioning

confidence: 99%

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

2022

IJIES

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“…An alternative approach based on machine learning has been proposed by Haas et al [63] In contrast to conventional image processing, features are not extracted manually, but the program learns to identify bubbles based on a labeled training data set. The detector, called BubCNN, shown in Figure 6, employs two pretrained modules, a Faster region-based convolutional neural network (RCNN) [64] and a shape regression CNN.…”

Section: Bubcnnmentioning

confidence: 99%

“…It should also be considered that the detection missing rate of automatic detectors depends on the local void fraction. [63] Therefore, the measurement uncertainty might be slightly higher at lower heights because the local void fraction is highest close to the porous plug. The bubble size distributions obtained by the different techniques are shown in Figure 7(b).…”

Section: E Uncertainty Quantificationmentioning

confidence: 99%

Toward a Strong-Sense Validation Benchmark Database for Numerical Ladle Flow Models

Haas

Suarez

Eickhoff

et al. 2020

Metall Mater Trans B

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In numerical computation of ladle metallurgy, multiphase models are essential. Still, these models are afflicted with great uncertainty, making a validation with experimental data mandatory. Validation experiments fundamentally differ from physical modeling experiments because emphasis is on a complete documentation of all boundary conditions and a detailed uncertainty assessment. For this work, the experimental design for a comprehensive accuracy assessment of numerical models and reported quantities were jointly determined with international numerical experts. The framework comprises a plume analysis and flow measurements in the single- and multiphase regions of a water model with defined conditions. All influencing factors are documented extensively and their contribution to the overall data uncertainty is quantified. Detailed data are made publicly available within a validation benchmark database for isothermal multiphase flow in metallurgical ladles. A first draft of a standardized validation procedure, including a single number validation score, is proposed. Using the available data, the accuracy of numerical models can be quantified more accurately and comparably, which helps in advancing the model’s further development. It also lays the foundation for a standardization of the validation process, which can lead to greater acceptance of the numerical results.

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“…Especially in microflow gauging, the bubble size and velocity are monitored in real time and compensated for in the fluid volume, which is a relatively effective method. Many technologies are currently adopted for the detection of microbubbles in tubes, such as capacitive detection [7], photoelectric detection [8], ultrasonic detection [9], image processing [10,11], etc. For capacitive detection, a high detection accuracy is difficult to obtain, and the photoelectric detection method is affected by the liquid's color.…”

Section: Introductionmentioning

confidence: 99%

A Fast Bubble Detection Method in Microtubes Based on Pulsed Ultrasound

et al. 2021

Micromachines

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In the process of biological microfluidic manipulation, the bubbles generated in the tube will seriously reduce the gauging accuracy. This paper introduces an improving method that can estimate the size of microbubbles in real time. Hence, the measurement data of the liquid volume can be modified according to this method. A microbubble detector based on the pulsed-ultrasound method was studied, including the device structure and the working principle. The assessment formula of the microbubbles in the tube was derived from the simulation results, which adopted the two-phase theory. The digital image processing method was applied to fulfill the microbubble calibration. This detection method was applied to measure the microbubbles in the tube and to modify the flow volume in a timely manner. The results of the experiments showed that this method is effective at improving the microflow gauging accuracy.

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BubCNN: Bubble detection using Faster RCNN and shape regression network

Cited by 106 publications

References 7 publications

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

Dysgraphia Classification based on the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network

Toward a Strong-Sense Validation Benchmark Database for Numerical Ladle Flow Models

A Fast Bubble Detection Method in Microtubes Based on Pulsed Ultrasound

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