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

arXiv (Cornell University)

Abstract

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.

Authors 5

1. Mohsen Vadidar
   Affiliation as printed

   Elektronische Fahrw- erksysteme GmbH , 85080 Gaimersheim ,

2. Ali Kariminezhad Aachen Research area Vehicle Intelligence & Automated Driving Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Elektronische Fahrw- erksysteme GmbH , 85080 Gaimersheim ,

   research area Vehicle Intelligence & Automated Driving , Institute for Automotive Engineering , RWTH Aachen University , 52074 Aachen , Germany

3. Christian Mayr
   Affiliation as printed

   Elektronische Fahrw- erksysteme GmbH , 85080 Gaimersheim ,

4. Laurent Kloeker
   Affiliation as printed

   Elektronische Fahrw- erksysteme GmbH , 85080 Gaimersheim ,

5. Lutz Eckstein
   Affiliation as printed

   Elektronische Fahrw- erksysteme GmbH , 85080 Gaimersheim ,