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

TL;DR

This paper explores the use of snapshot hyperspectral imaging combined with machine learning to detect macro-plastic litter in rivers, aiming for efficient, near-real-time monitoring of plastic pollution.

Contribution

It demonstrates the feasibility of using hyperspectral imaging and nonlinear classifiers for accurate detection of submerged plastics in river scenarios, advancing automated monitoring methods.

Findings

High detection accuracy achieved with hyperspectral data

Machine learning classifiers improve detection in challenging conditions

Code and data are publicly available for further research

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.