Robust 3D Object Detection in Cold Weather Conditions
- URL: http://arxiv.org/abs/2205.11925v1
- Date: Tue, 24 May 2022 09:37:07 GMT
- Title: Robust 3D Object Detection in Cold Weather Conditions
- Authors: Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner,
Johannes Kopp, Klaus Dietmayer
- Abstract summary: Adverse weather conditions can negatively affect LiDAR-based object detectors.
We focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions.
We propose to solve this problem by using data augmentation and a novel training loss term.
- Score: 7.924836086640871
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adverse weather conditions can negatively affect LiDAR-based object
detectors. In this work, we focus on the phenomenon of vehicle gas exhaust
condensation in cold weather conditions. This everyday effect can influence the
estimation of object sizes, orientations and introduce ghost object detections,
compromising the reliability of the state of the art object detectors. We
propose to solve this problem by using data augmentation and a novel training
loss term. To effectively train deep neural networks, a large set of labeled
data is needed. In case of adverse weather conditions, this process can be
extremely laborious and expensive. We address this issue in two steps: First,
we present a gas exhaust data generation method based on 3D surface
reconstruction and sampling which allows us to generate large sets of gas
exhaust clouds from a small pool of labeled data. Second, we introduce a point
cloud augmentation process that can be used to add gas exhaust to datasets
recorded in good weather conditions. Finally, we formulate a new training loss
term that leverages the augmented point cloud to increase object detection
robustness by penalizing predictions that include noise. In contrast to other
works, our method can be used with both grid-based and point-based detectors.
Moreover, since our approach does not require any network architecture changes,
inference times remain unchanged. Experimental results on real data show that
our proposed method greatly increases robustness to gas exhaust and noisy data.
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