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Marine & Wildlife
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SMACC: A System for Microplastics Automatic Counting and Classification
IEEE Access2020
69 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 50
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
May Gómez,
Javier Lorenzo-Navarro,
Javier Lorenzo-Navarro,
Javier Lorenzo-Navarro,
Javier Lorenzo-Navarro,
Javier Lorenzo-Navarro,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Ico Martínez
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Ico Martínez
Eugenio Raymond,
Ico Martínez
May Gómez,
May Gómez,
Alicia Herrera,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Modesto Castrillón-Santana,
Eugenio Raymond,
Alicia Herrera,
Modesto Castrillón-Santana,
Alicia Herrera,
Alicia Herrera,
Modesto Castrillón-Santana,
May Gómez,
Alicia Herrera,
Ico Martínez
Alicia Herrera,
Alicia Herrera,
Ico Martínez
Alicia Herrera,
Alicia Herrera,
Ico Martínez
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
May Gómez,
May Gómez,
Enrico Santesarti,
Modesto Castrillón-Santana,
Javier Lorenzo-Navarro,
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Alicia Herrera,
Ico Martínez
Ico Martínez
Alicia Herrera,
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Eugenio Raymond,
Eugenio Raymond,
Maria De Marsico,
May Gómez,
May Gómez,
Alicia Herrera,
Ico Martínez
Ico Martínez
May Gómez,
May Gómez,
May Gómez,
Ico Martínez
Ico Martínez
Ico Martínez
May Gómez,
Ico Martínez
May Gómez,
Ico Martínez
Ico Martínez
Ico Martínez
Modesto Castrillón-Santana,
Modesto Castrillón-Santana,
Ico Martínez
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
Ico Martínez
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Ico Martínez
May Gómez,
May Gómez,
Alicia Herrera,
May Gómez,
Eugenio Raymond,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
Ico Martínez
Alicia Herrera,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
May Gómez,
May Gómez,
Alicia Herrera,
Alicia Herrera,
Alicia Herrera,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
May Gómez,
Ico Martínez
Alicia Herrera,
Ico Martínez
Summary
Researchers developed an automated computer vision system (SMACC) that uses image analysis to count and classify plastic particles in beach samples, demonstrating that machine learning can substantially reduce the time and effort required for large-scale beach microplastic monitoring.
Study Type
Environmental
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.