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Enhancing the Performance of a Rainfall Measurement System Using Artificial Neural Networks Based Object Tracking Algorithms

Chen, Chih-Yen, Wang, Lijuan, Hwang, Chi-Hung, Hsieh, Chi-Wen, Chi, Po-Wei (2019) Enhancing the Performance of a Rainfall Measurement System Using Artificial Neural Networks Based Object Tracking Algorithms. In: UNSPECIFIED. (doi:10.1109/I2MTC.2019.8827108) (KAR id:77850)

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With the recent development of optical sensing and digital image processing techniques, high-speed cameras have been applied to measure the microphysical properties of raindrops. However, the performance of such systems are significantly affected by object tracking algorithms. In order to improve the measurement accuracy of rainfall rate and accumulated rainfall, a novel object tracking algorithm based on artificial neural networks (ANN) is proposed in this paper. The ANN model takes the features of each raindrop in the two successive images as inputs including the center coordinates, area, canting angle, the lengths of long axis and minor axis of the equivalent ellipse. The output of the ANN model is the matched probabilities of each pair of raindrops between before and after images. Experimental data were collected during a real rainfall event. Performance comparisons between the traditional and ANN based object tracking algorithms are conducted based on the experimental data. Experimental results suggest the successful matching rate is significantly increased from 87.20% to 95.60% due to the usage of the ANN based algorithm. Hence, the improved disdrometer system is capable of producing more accurate and robust measurements of rainfall status.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/I2MTC.2019.8827108
Uncontrolled keywords: rainfall measurement, high-speed camera, disdrometer, object tracking algorithm, artificial neural network
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Lijuan Wang
Date Deposited: 25 Oct 2019 16:38 UTC
Last Modified: 09 Dec 2022 00:21 UTC
Resource URI: (The current URI for this page, for reference purposes)
Wang, Lijuan:
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