Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challen...

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Bibliographic Details
Published in:Journal of environmental management Vol. 359; p. 120954
Main Authors: Han, Kaitai, Huang, Mengyuan, Wang, Zhenghui, Shi, Chaojing, Wang, Zijun, Guo, Jialu, Liu, Wu, Lei, Lixin, Guo, Qianjin
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.05.2024
Subjects:
Automated data processing pipeline
automation
data collection
Deep learning model
environmental management
Microplastic detection
Microplastic pollution
microplastics
Photoacoustic imaging
Shape classification
wastes
Shape classification
Microplastic pollution
Photoacoustic imaging
Deep learning model
Automated data processing pipeline
Microplastic detection
ISSN:0301-4797, 1095-8630, 1095-8630
Online Access:Get full text
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Summary:Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics. [Display omitted] •Innovative microplastics detection using high resolution photoacoustic imaging method.•Automated SAM enhances detection efficiency and morphology characterization analysis.•Vqdp2 via multi-task parameter constraints to improve shape classification accuracy.
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2024.120954