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.

在本文中，我们通过计算成像方法分析了在河流情景下利用大气塑料垃圾的宏观可视-短波红外高光谱成像进行监测的可行性；我们的实验表明，结合机器学习分类方法和高光谱数据以及非线性分类器，可以在具有挑战性的情景中实现高精度的检测。

清洁河流的愿景：利用快照式高光谱成像技术检测大型塑料垃圾