3 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Lei Yang, Xinyu Zhang, Jun Li +5
Roadside perception can greatly increase the safety of autonomous vehicles by extending their perception ability beyond the visual range and addressing blind spots. However, curren…
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Yan Gong, Xinyu Zhang, Hao Liu +7
Multi-modal fusion is increasingly being used for autonomous driving tasks, as different modalities provide unique information for feature extraction. However, the existing two-str…
Informative Data Selection with Uncertainty for Multi-modal Object Detection
Xinyu Zhang, Zhiwei Li, Zhenhong Zou +5
Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurat…
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
Li Wang, Xinyu Zhang, Wenyuan Qin +7
3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However,…
IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Huanan Wang, Xinyu Zhang, Jun Li +4
Due to the high complexity and occlusion, insufficient perception in the crowded urban intersection can be a serious safety risk for both human drivers and autonomous algorithms, w…
A novel multimodal fusion network based on a joint coding model for lane line segmentation
Zhenhong Zou, Xinyu Zhang, Huaping Liu +3
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architectur…