Publikation
Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion
Lukas Stefan Stäcker; Philipp Heidenreich; Jason Raphael Rambach; Didier Stricker
In: 2022 Proceedings of the. IEEE Winter Conference on Applications of Computer Vision (WACV-2022), January 4-8, Hawaii, HI, USA, IEEE, 2022.
Zusammenfassung
Object detection is one of the most important perception
tasks for advanced driver assistant systems and autonomous
driving. Due to its complementary features and
moderate cost, radar-camera fusion is of particular interest
in the automotive industry but comes with the challenge
of how to optimally fuse the heterogeneous data sources. To
solve this for 2D object detection, we propose two new techniques
to project the radar detections onto the image plane,
exploiting additional uncertainty information. We also introduce
a new technique called fusion point pruning, which
automatically finds the best fusion points of radar and image
features in the neural network architecture. These new
approaches combined surpass the state of the art in 2D object
detection performance for radar-camera fusion models,
evaluated with the nuScenes dataset. We further find that
the utilization of radar-camera fusion is especially beneficial
for night scenes.