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Found 3 papers, 1 papers with code
Real-time Traffic Object Detection for Autonomous Driving
In this research, we assess the robustness of our previously proposed, highly efficient pedestrian detector LSFM on well-established autonomous driving benchmarks, including diverse weather conditions and nighttime scenes.
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving
Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in real-time.
Ranked #1 on
Pedestrian Detection
on TJU-Ped-traffic
F2DNet: Fast Focal Detection Network for Pedestrian Detection
However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i. e. in region proposal networks and bounding box heads.
Ranked #2 on
Pedestrian Detection
on Caltech
(using extra training data)
