PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

TL;DR

PoTATO is a new dataset that leverages polarimetric imaging to improve the detection of floating plastic waste, addressing challenges faced by traditional methods under outdoor lighting conditions.

Contribution

The paper introduces PoTATO, a comprehensive dataset with polarimetric data for plastic waste detection, enabling research into polarization-based object recognition in aquatic environments.

Findings

Polarization can significantly improve detection accuracy under certain conditions.

The dataset includes 12,380 labeled plastic bottles with rich polarimetric information.

Open access to data and code facilitates further research in this domain.

Abstract

Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024.