Ricerc@Sapienza

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.