SMACC: A System for Microplastics Automatic Counting and Classification
The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the pl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8976153/ |
| _version_ | 1831766562899492864 |
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| author | Javier Lorenzo-Navarro Modesto Castrillon-Santana Enrico Santesarti Maria De Marsico Ico Martinez Eugenio Raymond May Gomez Alicia Herrera |
| author_facet | Javier Lorenzo-Navarro Modesto Castrillon-Santana Enrico Santesarti Maria De Marsico Ico Martinez Eugenio Raymond May Gomez Alicia Herrera |
| author_sort | Javier Lorenzo-Navarro |
| collection | DOAJ |
| description | The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very time-consuming task. In this work, we present a Computer Vision-based system which is able to automatically count and classify microplastic particles (1-5 mm) into five different visual classes. After cleaning a collected sample in the lab, the proposed system makes use of a pair of its images with different characteristics. The procedure includes a segmentation step, which is based on the Sauvola thresholding method, followed by a feature extraction and classification step. Different features and classifiers are evaluated as well as a deep learning approach. The system is tested on 12 different beach samples with a total of 2507 microplastic particles. The particles of each sample were manually counted and sorted by an expert. This data represents the ground truth, which is compared later with the results of the automatic processing proposals to evaluate their accuracy. The difference in the number of particles is 34 (1.4%) and the error in their classification is less than 4% for all types except for the line shapes particles. These results are obtained in less than half of the time needed by the human expert doing the same task manually. This implies that it is possible to process more than twice as many samples using the same time, allowing the biologists to monitor wider areas and more frequently than doing the process manually. |
| first_indexed | 2024-12-22T06:25:39Z |
| format | Article |
| id | doaj.art-7ebea853bb7f468caaefe6e2c4ed9afc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T06:25:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7ebea853bb7f468caaefe6e2c4ed9afc2022-12-21T18:35:51ZengIEEEIEEE Access2169-35362020-01-018252492526110.1109/ACCESS.2020.29704988976153SMACC: A System for Microplastics Automatic Counting and ClassificationJavier Lorenzo-Navarro0https://orcid.org/0000-0002-2834-2067Modesto Castrillon-Santana1https://orcid.org/0000-0002-8673-2725Enrico Santesarti2https://orcid.org/0000-0003-2504-6753Maria De Marsico3https://orcid.org/0000-0002-1391-8502Ico Martinez4https://orcid.org/0000-0002-7676-2066Eugenio Raymond5https://orcid.org/0000-0003-1563-7322May Gomez6https://orcid.org/0000-0002-7396-6493Alicia Herrera7https://orcid.org/0000-0002-5538-6161Instituto Universitario SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainInstituto Universitario SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainDipartimento di Informatica, Sapienza Universita di Roma, Rome, ItalyDipartimento di Informatica, Sapienza Universita di Roma, Rome, ItalyEOMAR Group, Instituto Universitario ECOAQUA, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainEOMAR Group, Instituto Universitario ECOAQUA, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainEOMAR Group, Instituto Universitario ECOAQUA, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainEOMAR Group, Instituto Universitario ECOAQUA, Universidad de Las Palmas de Gran Canaria, Las Palmas, SpainThe management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very time-consuming task. In this work, we present a Computer Vision-based system which is able to automatically count and classify microplastic particles (1-5 mm) into five different visual classes. After cleaning a collected sample in the lab, the proposed system makes use of a pair of its images with different characteristics. The procedure includes a segmentation step, which is based on the Sauvola thresholding method, followed by a feature extraction and classification step. Different features and classifiers are evaluated as well as a deep learning approach. The system is tested on 12 different beach samples with a total of 2507 microplastic particles. The particles of each sample were manually counted and sorted by an expert. This data represents the ground truth, which is compared later with the results of the automatic processing proposals to evaluate their accuracy. The difference in the number of particles is 34 (1.4%) and the error in their classification is less than 4% for all types except for the line shapes particles. These results are obtained in less than half of the time needed by the human expert doing the same task manually. This implies that it is possible to process more than twice as many samples using the same time, allowing the biologists to monitor wider areas and more frequently than doing the process manually.https://ieeexplore.ieee.org/document/8976153/Computer visiondeep learningmicroplastics classification |
| spellingShingle | Javier Lorenzo-Navarro Modesto Castrillon-Santana Enrico Santesarti Maria De Marsico Ico Martinez Eugenio Raymond May Gomez Alicia Herrera SMACC: A System for Microplastics Automatic Counting and Classification IEEE Access Computer vision deep learning microplastics classification |
| title | SMACC: A System for Microplastics Automatic Counting and Classification |
| title_full | SMACC: A System for Microplastics Automatic Counting and Classification |
| title_fullStr | SMACC: A System for Microplastics Automatic Counting and Classification |
| title_full_unstemmed | SMACC: A System for Microplastics Automatic Counting and Classification |
| title_short | SMACC: A System for Microplastics Automatic Counting and Classification |
| title_sort | smacc a system for microplastics automatic counting and classification |
| topic | Computer vision deep learning microplastics classification |
| url | https://ieeexplore.ieee.org/document/8976153/ |
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