ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under
Challenging Conditions
- URL: http://arxiv.org/abs/2308.10161v3
- Date: Tue, 12 Sep 2023 09:45:02 GMT
- Title: ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under
Challenging Conditions
- Authors: Qiao Yan, Yihan Wang
- Abstract summary: We present a new multi-modal dataset called ThermRad, which includes a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera.
We propose a new multi-modal fusion method called RTDF-RCNN, which leverages the complementary strengths of 4D radars and thermal cameras to boost object detection performance.
Our method achieves significant enhancements in detecting cars, pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%, respectively.
- Score: 15.925365473140479
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robust 3D object detection in extreme weather and illumination conditions is
a challenging task. While radars and thermal cameras are known for their
resilience to these conditions, few studies have been conducted on
radar-thermal fusion due to the lack of corresponding datasets. To address this
gap, we first present a new multi-modal dataset called ThermRad, which includes
a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera. This dataset is
unique because it includes data from all four sensors in extreme weather
conditions, providing a valuable resource for future research in this area. To
validate the robustness of 4D radars and thermal cameras for 3D object
detection in challenging weather conditions, we propose a new multi-modal
fusion method called RTDF-RCNN, which leverages the complementary strengths of
4D radars and thermal cameras to boost object detection performance. To further
prove the effectiveness of our proposed framework, we re-implement
state-of-the-art (SOTA) 3D detectors on our dataset as benchmarks for
evaluation. Our method achieves significant enhancements in detecting cars,
pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%,
respectively, while achieving comparable results to LiDAR-based approaches. Our
contributions in both the ThermRad dataset and the new multi-modal fusion
method provide a new approach to robust 3D object detection in adverse weather
and illumination conditions. The ThermRad dataset will be released.
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