Traffic Object Detection
10 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Traffic Object Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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BDD100K
Car datasets in multiple scenes
Most implemented papers
You Only Learn One Representation: Unified Network for Multiple Tasks
WongKinYiu/yolor
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In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning.
YOLOP: You Only Look Once for Panoptic Driving Perception
hustvl/yolop
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A panoptic driving perception system is an essential part of autonomous driving.
HybridNets: End-to-End Perception Network
Based on these optimizations, we have developed an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously, called HybridNets, which achieves better accuracy than prior art.
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
china-ai-law-challenge/CAIL2019
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In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset.
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
CAIC-AD/YOLOPv2
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Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance.
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection
leofansq/FII-CenterNet
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IEEE Transactions on Vehicular Technology 2021
Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects.
You Only Look at Once for Real-time and Generic Multi-Task
jiayuanwang-jw/yolov8-multi-task
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In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks.
Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion
To address this issue, we introduce bio-inspired event cameras and propose a novel Structure-aware Fusion Network (SFNet) that extracts sharp and complete object structures from the event stream to compensate for the lost information in images through cross-modality fusion, enabling the network to obtain illumination-robust representations for traffic object detection.
Performance Evaluation of Real-Time Object Detection for Electric Scooters
Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges.
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios.
