A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

¹ Laboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco.
² PMIC Laboratory, FST Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco.
³ Faculty of science tetouan, Abdelmalek Essaadi University, Tetouan, Morocco.

^* e-mail: abdellah.elzaar@gmail.com
^** e-mail: aoulalayayoub@gmail.com
^*** e-mail: nabil.benaya@gmail.com
^**** e-mail: abderrahim.elmhouti@gmail.com
^† e-mail: massarmed@hotmail.com
^‡ e-mail: abdou.allati@gmail.com

Abstract

The accumulation of plastic objects in the Earth’s environment will adversely affect wildlife, wildlife habitat, and humans. The huge amount of unrecycled plastic ends up in landfill and thrown into unregulated dump sites. In many cases, specifically in the developing countries, plastic waste is thrown into rivers, streams and oceans. In this work, we employed the power of deep learning techniques in image processing and classification to recognize plastic waste. Our work aims to identify plastic texture and plastic objects in images in order to reduce plastic waste in the oceans, and facilitate waste management. For this, we use transfer learning in two ways: in the first one, a pre-trained CNN model on ImageNet is used as a feature extractor, then an SVM classifier for classification, the second strategy is based on fine tuning the pre-trained CNN model. Our approach was trained and tested using two (02) challenging datasets one is a texture recognition dataset and the other is for object detection, and achieves very satisfactory results using two (02) deep learning strategies.