Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42146
Title: Automatic counting and classification of microplastic particles
Authors: Lorenzo-Navarro, Javier 
Castrillon-Santana, Modesto 
Gómez, May 
Herrera, Alicia 
Marín-Reyes, Pedro A.
UNESCO Clasification: 251001 Oceanografía biológica
120304 Inteligencia artificial
Keywords: Microplastics
Beach pollution
Automatic counting
Microplastics classification.
Issue Date: 2018
Journal: ICPRAM 2018 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods
Conference: 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM) 
7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018 
Abstract: Microplastic particles have become an important ecological problem due to the huge amount of plastics debris that ends up in the sea. An additional impact is the ingestion of microplastics by marine species, and thus microplastics enter into the food chain with unpredictable effects on humans. In addition to the exploration of their presence in fishes, researchers are studying the presence of microplastics in coastal areas. The workload is therefore time consuming, due to the need to carry out regular campaigns to quantify their presence in the samples. So, in this work a method for automatic counting and classifying microplastic particles is presented. To the best of our knowledge, this is the first proposal to address this challenging problem. The method makes use of Computer Vision techniques for analyzing the acquired images of the samples; and Machine Learning techniques to develop accurate classifiers of the different types of microplastic particles that are considered. The obtained results show that making use of color based and shape based features along with a Random Forest classifier, an accuracy of 96.6% is achieved recognizing four types of particles: pellets, fragments, tar and line.
URI: http://hdl.handle.net/10553/42146
ISBN: 9789897582769
DOI: 10.5220/0006725006460652
Source: ICPRAM 2018 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods,v. 2018-January, p. 646-652
Appears in Collections:Actas de congresos
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