A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light
Abstract Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a labor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53251-5 |
| _version_ | 1797274799312994304 |
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| author | Thunchanok Thammasanya Sakarat Patiam Eknarin Rodcharoen Ponlachart Chotikarn |
| author_facet | Thunchanok Thammasanya Sakarat Patiam Eknarin Rodcharoen Ponlachart Chotikarn |
| author_sort | Thunchanok Thammasanya |
| collection | DOAJ |
| description | Abstract Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a laboratory. However, deep learning can assist in identifying and quantifying microplastics from an image. This paper presents a novel microplastic classification method that combines the benefits of UV light with deep learning. The Faster-RCNN model with a ResNet-50-FPN backbone was implemented to detect and identify microplastics. Microplastic images from the field taken under UV light were used to train and validate the model. This classification model achieved a high precision of 85.5–87.8%, and the mAP scores were 33.9% on an internal test set and 35.7% on an external test set. This classification approach provides a high-accuracy, low-cost, and time-effective automated identification and counting of microplastics. |
| first_indexed | 2024-03-07T15:03:10Z |
| format | Article |
| id | doaj.art-3c7eac3075cf4d00af5169cc3424daef |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:03:10Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3c7eac3075cf4d00af5169cc3424daef2024-03-05T19:00:25ZengNature PortfolioScientific Reports2045-23222024-02-0114111110.1038/s41598-024-53251-5A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet lightThunchanok Thammasanya0Sakarat Patiam1Eknarin Rodcharoen2Ponlachart Chotikarn3Faculty of Environmental Management, Prince of Songkla UniversityAquatic Science and Innovative Management Division, Faculty of Natural Resources, DoE for Sustainable Aquaculture, Prince of Songkla UniversityAquatic Science and Innovative Management Division, Faculty of Natural Resources, DoE for Sustainable Aquaculture, Prince of Songkla UniversityFaculty of Environmental Management, Prince of Songkla UniversityAbstract Hazardous compounds from microplastics in coastal and marine environments are adsorbed by live organisms, affecting human and marine life. It takes time, money and effort to study the distribution and type of microplastics in the environment, using appropriate expensive equipment in a laboratory. However, deep learning can assist in identifying and quantifying microplastics from an image. This paper presents a novel microplastic classification method that combines the benefits of UV light with deep learning. The Faster-RCNN model with a ResNet-50-FPN backbone was implemented to detect and identify microplastics. Microplastic images from the field taken under UV light were used to train and validate the model. This classification model achieved a high precision of 85.5–87.8%, and the mAP scores were 33.9% on an internal test set and 35.7% on an external test set. This classification approach provides a high-accuracy, low-cost, and time-effective automated identification and counting of microplastics.https://doi.org/10.1038/s41598-024-53251-5 |
| spellingShingle | Thunchanok Thammasanya Sakarat Patiam Eknarin Rodcharoen Ponlachart Chotikarn A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light Scientific Reports |
| title | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light |
| title_full | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light |
| title_fullStr | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light |
| title_full_unstemmed | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light |
| title_short | A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light |
| title_sort | new approach to classifying polymer type of microplastics based on faster rcnn fpn and spectroscopic imagery under ultraviolet light |
| url | https://doi.org/10.1038/s41598-024-53251-5 |
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