Exploiting Polarized Material Cues for Robust Car Detection
Wen Dong Haiyang Mei Ziqi Wei Ao Jin Sen Qiu Qiang Zhang Xin Yang
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.