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The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within thevisible light spectrum. Therefore it is very sensitive to environmental lightconditions. On the contrary, a long-wave infrared (LWIR) sensor operating in8-14 micro meter spectral band, functions independent of visible light.  In this paper, we exploit both visual and thermal perception units for robustobject detection purposes. After delicate synchronization and (cross-) labelingof the FLIR [1] dataset, this multi-modal perception data passes through aconvolutional neural network (CNN) to detect three critical objects on theroad, namely pedestrians, bicycles, and cars. After evaluation of RGB andinfrared (thermal and infrared are often used interchangeably) sensorsseparately, various network structures are compared to fuse the data at thefeature level effectively. Our RGB-thermal (RGBT) fusion network, which takesadvantage of a novel entropy-block attention module (EBAM), outperforms thestate-of-the-art network [2] by 10% with 82.9% mAP.

Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection