Polarimetric imaging, along with deep learning, has shown improved
performances on different tasks including scene analysis. However, its
robustness may be questioned because of the small size of the training
datasets. Though the issue could be solved by data augmentation, polarization
modalities are subject to physical feasibility constraints unaddressed by
classical data augmentation techniques. To address this issue, we propose to
use CycleGAN, an image translation technique based on deep generative models
that solely relies on unpaired data, to transfer large labeled road scene
datasets to the polarimetric domain. We design several auxiliary loss terms
that, alongside the CycleGAN losses, deal with the physical constraints of
polarimetric images. The efficiency of this solution is demonstrated on road
scene object detection tasks where generated realistic polarimetric images
allow to improve performances on cars and pedestrian detection up to 9%. The
resulting constrained CycleGAN is publicly released, allowing anyone to
generate their own polarimetric images.

本文提出使用基于深度生成模型的图像翻译技术 CycleGAN 来将大型标记路景数据集转换到极化领域，通过一些附加损失项来处理极化图像的物理约束条件，从而实现在道路场景的物体检测任务中提高汽车和行人检测的性能高达 9％的目标，该方法已经公开发布以供其他人使用。

道路场景分析的物理可信偏振数据增强

Physically-admissible polarimetric data augmentation for road-scene analysis

Object detection in road scenes is necessary to develop both autonomous
vehicles and driving assistance systems. Even if deep neural networks for
recognition task have shown great performances using conventional images, they
fail to detect objects in road scenes in complex acquisition situations. In
contrast, polarization images, characterizing the light wave, can robustly
describe important physical properties of the object even under poor
illumination or strong reflections. This paper shows how non-conventional
polarimetric imaging modality overcomes the classical methods for object
detection especially in adverse weather conditions. The efficiency of the
proposed method is mostly due to the high power of the polarimetry to
discriminate any object by its reflective properties and on the use of deep
neural networks for object detection. Our goal by this work, is to prove that
polarimetry brings a real added value compared with RGB images for object
detection. Experimental results on our own dataset composed of road scene
images taken during adverse weather conditions show that polarimetry together
with deep learning can improve the state-of-the-art by about 20% to 50% on
different detection tasks.

本篇论文研讨了一种基于极化成像模态的新型物体识别技术，证明该技术在恶劣气候条件下比 RGB 图像更具有效性，能有效提高不同检测任务的状态机会 20% 至 50%。