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Huang H, Cai H, Qureshi JU, Mehdi SR, Song H, Liu C, Di Y, Shi H, Yao W, Sun Z. Proceeding the categorization of microplastics through deep learning-based image segmentation. Sci Total Environ 2023; 896:165308. [PMID: 37414186 DOI: 10.1016/j.scitotenv.2023.165308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/01/2023] [Accepted: 07/02/2023] [Indexed: 07/08/2023]
Abstract
Microplastics (MPs) have been recognized as prominent anthropogenic pollutants that inflict significant harm to marine ecosystems. Various approaches have been proposed to mitigate the risks posed by MPs. Gaining an understanding of the morphology of plastic particles can provide valuable insights into the source and their interaction with marine organisms, which can assist the development of response measures. In this study, we present an automated technique for identifying MPs through segmentation of MPs in microscopic images using a deep convolutional neural network (DCNN) based on a shape classification nomenclature framework. We used MP images from diverse samples to train a Mask Region Convolutional Neural Network (Mask R-CNN) based model for classification. Erosion and dilation operations were added to the model to improve segmentation results. On the testing dataset, the mean F1-score (F1) of segmentation and shape classification was 0.7601 and 0.617, respectively. These results demonstrate the potential of proposed method for the automatic segmentation and shape classification of MPs. Furthermore, by adopting a specific nomenclature, our approach represents a practical step towards the global standardization of MPs categorization criteria. This work also identifies future research directions to improve accuracy and further explore the possibilities of using DCNN for MPs identification.
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Affiliation(s)
- Hui Huang
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China; Hainan Institute of Zhejiang University, Sanya 572024, Hainan, PR China
| | - Huiwen Cai
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, PR China
| | | | - Syed Raza Mehdi
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China
| | - Hong Song
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China
| | - Caicai Liu
- East China Sea Bureau of the Ministry of Natural Resources, Shanghai 200137, PR China
| | - Yanan Di
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China.
| | - Huahong Shi
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, PR China
| | - Weimin Yao
- East China Sea Bureau of the Ministry of Natural Resources, Shanghai 200137, PR China
| | - Zehao Sun
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China
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Huang H, Qureshi JU, Liu S, Sun Z, Zhang C, Wang H. Hyperspectral Imaging as a Potential Online Detection Method of Microplastics. Bull Environ Contam Toxicol 2021; 107:754-763. [PMID: 32556690 DOI: 10.1007/s00128-020-02902-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/09/2020] [Indexed: 06/11/2023]
Abstract
Microplastic pollution in aquatic environment has raised concern and as a result a number of studies have recently been published to find solutions for its rapid increase. Different methods have been proposed for microplastic identification. Spectral imaging shows a lot of promise for polymer identification; however, the identification time needs to be improved. Hyperspectral imaging (HSI) combined with chemometric analysis can reduce the identification times. In this study, we provide a review of recent studies related to polymer identification using HSI with a focus on the adopted classification algorithm and its factors for the online implementation of HSI. Furthermore, we review the limit of detection by HSI and the effect of particle size on classification accuracy. Additionally, performance of this method for various types of samples is also discussed. We conclude that HSI is possible to be a fast alternative for online microplastic detection.
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Affiliation(s)
- Hui Huang
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
- The Engineering Rresearch Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhoushan, 316021, Zhejiang, China
- Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Zhoushan, 316021, Zhejiang, China
| | | | - Shuchang Liu
- Jacobs Engineering, University of California of San Diego, San Diego, CA, 92093, USA
| | - Zehao Sun
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
| | - Chunfang Zhang
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
- The Engineering Rresearch Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhoushan, 316021, Zhejiang, China
| | - Hangzhou Wang
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China.
- The Engineering Rresearch Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhoushan, 316021, Zhejiang, China.
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