RANDOM FOREST-BASED RIVER PLASTIC DETECTION WITH A HANDHELD MULTISPECTRAL CAMERA
Keywords: Multispectral camera, Plastic litter, Mapping, Random-forest, Object detection
Abstract. Plastic pollution has become one of the main global environmental emergencies. A considerable part of used plastics materials is dispersed or accumulated in the environment with a significant damaging impact on many terrestrial and aquatic ecosystems.
Artificial Intelligence has proven a fundamental approach in last years for the detection of plastics waste in the aquatic habitats: several groups have recently tried to tackle such problem by developing some machine learning-based methods and multispectral or RGB imagery. This study compares the results obtained by two machine learning classifiers, namely Random Forests and Support Vector Machine, to detect macroplastic in the fluvial habitat through multispectral imagery. The acquisition of images has been made with a hand-held multispectral camera called MAIA-WV2. Despite the obtained results are quite good in terms of accuracy in a random validation dataset, some issues, mostly related to the presence of white rocks and glares on water have still to be properly solved.