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A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter
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Researchers developed a near-real-time macro-plastic detection system using snapshot Visible-Shortwave Infrared hyperspectral imaging combined with machine learning classifiers to identify partially submerged plastic litter in river environments. Their experiments demonstrated high detection accuracy even in challenging conditions, with nonlinear classifiers and hyperspectral data outperforming conventional approaches.
Plastic waste entering the riverine harms local ecosystems leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automatized monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies associated with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://github.com/RIVeR-Lab/hyperspectral_macro_plastic_detection.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.