Spatio-temporal consistency for semi-supervised learning using 3D radar cubes
2021
Radar has been employed as a key component of
perception modules for more than two decades. However, radar
image labelling requires expert knowledge. At the same time,
it is much more time-consuming than for general RGB images,
which impedes further developments of radar.
In order to alleviate the high-cost annotation problem in
radar datasets, we present a novel, semi-supervised deep
learning method based on the spatio-temporal consistency. This
way we explore the potential of unlabeled radar frames to
enhance performance. We utilize the consecutive radar frames
from different timeline directions to encourage the model to
learn the target motion. Moreover, the proposed self-weighted
mechanism avoids over-fitting on certain predominant targets,
by exploiting the supervised classification loss dynamically.
We evaluate the proposed method on semantic segmentation
and Vulnerable Road Users (VRUs) detection problems. The
quantitative results compare favourably to the state-of-the-art
and demonstrate the effectiveness of the proposed concepts.
The ablation studies also show the effectiveness of the proposed
components.
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