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Deep learning approach for automatic microplastics counting and classification
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Researchers developed a deep learning architecture combining U-Net segmentation and VGG16 classification to automatically count and categorise microplastic particles of 1-5 mm into fragments, pellets, and lines from digital camera images. The system reduces the cost and time of traditional microplastic quantification methods while enabling high-throughput monitoring.
The quantification of microplastics is a needed task to monitor its evolution and model its behavior. However, it is a time demanding task traditionally performed using expensive equipment. In this paper, an architecture based on deep learning networks is presented with the aim of automatically count and classify microplastic particles in the range of 1-5 mm from pictures taken with a digital camera or a mobile phone with a resolution of 16 million pixels or higher. The proposed architecture comprises a first stage, implemented with the U-Net neural network, in charge of making the segmentation of the particles in the image. After the different particles have been isolated, a second stage based on the VGG16 neural network classifies them into three types: fragments, pellets and lines. These three types have been selected for being the most common in the range size under consideration. The experimental evaluation was carried out using images taken with two digital cameras and one mobile phone. The particles used in experiments correspond to samples collected on the beach of Playa del Poris in Tenerife Island, Spain, (28° 09' 51″ N, 16° 25' 54″ W) in August 2018. A Jaccard index value of 0.8 is achieved in the experiments of particles segmentation and an accuracy of 98.11% is obtained in the classification of the microplastic particles. The proposed architecture is remarkable faster than a similar previously published system based on traditional computer vision techniques.
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