A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter

• URL: http://arxiv.org/abs/2307.12145v1
• Date: Sat, 22 Jul 2023 18:59:27 GMT
• Title: A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter
• Authors: Nathaniel Hanson, Ahmet Demirkaya, Deniz Erdo\u{g}mu\c{s}, Aron Stubbins, Ta\c{s}k{\i}n Pad{\i}r, Tales Imbiriba
• Abstract summary: Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. We analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios.
• Score: 6.198237241838559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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