用偏振图像提升水面上垃圾的分割精度，减少反光干扰。
Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments
- 融合偏振信息增强低对比度物体检测
- 相比RGB图像，平均IoU提升，轮廓误差降低
- 适合水下视觉、环保监测等场景研究者参考
准确分割水面上的漂浮垃圾常受水面反光和光照变化影响。偏振成像提供了一种单传感器方案，可缓解水面反光对语义分割的干扰。我们在公开数据集PoTATO（内河航道塑料瓶偏振图像）上评估了先进融合网络性能，并与传统模型的单图基线进行比较。结果表明，偏振线索有助于恢复低对比度目标并抑制反射引起的误检，相比RGB输入，平均交并比（mean IoU）提升，轮廓误差下降。然而，更精细的掩码也带来代价：额外通道增加模型规模，提升计算负担，并可能引入新误检。我们提供可复现的诊断基准与开源代码，旨在帮助研究者判断偏振相机是否适用于其任务，并推动相关研究发展。
原文摘要 · Abstract (English)
Accurate segmentation of floating debris on water is often compromised by surface glare and changing outdoor illumination. Polarimetric imaging offers a single-sensor route to mitigate water-surface glare that disrupts semantic segmentation of floating objects. We benchmark state-of-the-art fusion networks on PoTATO, a public dataset of polarimetric images of plastic bottles in inland waterways, and compare their performance with single-image baselines using traditional models. Our results indicate that polarimetric cues help recover low-contrast objects and suppress reflection-induced false positives, raising mean IoU and lowering contour error relative to RGB inputs. These sharper masks come at a cost: the additional channels enlarge the models increasing the computational load and introducing the risk of new false positives. By providing a reproducible, diagnostic benchmark and publicly available code, we hope to help researchers choose if polarized cameras are suitable for their applications and to accelerate related research.
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