  The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the
visible light spectrum. Therefore it is very sensitive to environmental light
conditions. On the contrary, a long-wave infrared (LWIR) sensor operating in
8-14 micro meter spectral band, functions independent of visible light.
  In this paper, we exploit both visual and thermal perception units for robust
object detection purposes. After delicate synchronization and (cross-) labeling
of the FLIR [1] dataset, this multi-modal perception data passes through a
convolutional neural network (CNN) to detect three critical objects on the
road, namely pedestrians, bicycles, and cars. After evaluation of RGB and
infrared (thermal and infrared are often used interchangeably) sensors
separately, various network structures are compared to fuse the data at the
feature level effectively. Our RGB-thermal (RGBT) fusion network, which takes
advantage of a novel entropy-block attention module (EBAM), outperforms the
state-of-the-art network [2] by 10% with 82.9% mAP.
