Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detection algorithms to meet the highly accurate perception demand for safety, due to the unstable/limited color information, which impedes the extraction of meaningful/discriminative features of cars. In this work, we present a novel learning-based car detection method that leverages trichromatic linear polarization as an additional cue to disambiguate such challenging cases. A key observation is that polarization, characteristic of the light wave, can robustly describe intrinsic physical properties of the scene objects in various imaging conditions and is strongly linked to the nature of materials for cars (e.g., metal and glass) and their surrounding environment (e.g., soil and trees), thereby providing reliable and discriminative features for robust car detection in challenging scenes. To exploit polarization cues, we first construct a pixel-aligned RGB-Polarization car detection dataset, which we subsequently employ to train a novel multimodal fusion network. Our car detection network dynamically integrates RGB and polarization features in a request-and-complement manner and can explore the intrinsic material properties of cars across all learning samples. We extensively validate our method and demonstrate that it outperforms state-of-the-art detection methods. Experimental results show that polarization is a powerful cue for car detection.

翻译：车辆检测是一项重要任务，是众多自动驾驶功能的关键前提。由于光照/天气条件及场景车辆密度的巨大变化，现有车辆检测算法面临满足高精度安全感知需求的重大挑战，其根本原因在于不稳定/有限的色彩信息阻碍了车辆有意义/判别性特征的提取。本文提出一种新颖的基于学习的车辆检测方法，利用三色线性偏振作为额外线索来消除此类困难场景中的歧义。关键发现是：作为光波特性的偏振，能在各种成像条件下鲁棒描述场景物体的固有物理属性，并与车辆（如金属和玻璃）及其周围环境（如土壤和树木）的材料本质紧密相关，从而为复杂场景下的鲁棒车辆检测提供可靠且具有判别性的特征。为利用偏振线索，我们首先构建像素对齐的RGB-偏振车辆检测数据集，并据此训练新型多模态融合网络。该车辆检测网络通过请求与补充机制动态整合RGB与偏振特征，能够跨所有学习样本探索车辆的固有材料属性。我们通过大量实验验证该方法，证明其优于现有最优检测方法。实验结果表明，偏振是车辆检测的有力线索。