用偏振信息提升水面垃圾识别准确率，发布1.2万张标注数据集。
PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects
- 利用偏振成像增强水面对漂浮塑料瓶的检测能力。
- 数据集含12,380张标注图像，覆盖复杂光照与反光条件。
- 适合研究计算机视觉、环境机器人及遥感检测的学者使用。
水体中的塑料污染对海洋生物和人类健康构成严重威胁。自主机器人可用于收集漂浮垃圾，但其准确识别能力受限于户外光照条件和水面反光。光偏振在这些环境中丰富存在，却人眼不可见，现代传感器可捕捉该信息以显著提升水面上垃圾检测的准确性。为此，我们提出了PoTATO数据集，包含12,380个标注的塑料瓶样本及丰富的偏振信息。我们验证了偏振在何种条件下能有效提升目标检测性能，并通过提供原始图像数据，为研究社区探索新方法、推动先进目标检测算法发展提供了可能。代码与数据可在https://github.com/luisfelipewb/PoTATO/tree/eccv2024获取。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。