Papers with Code · CC BY-SA 4.0

Drivable Area Detection

2D Object Detection › Drivable Area Detection 10 평가 기록 1 datasets

The drivable area detection is a subset topic of object detection. The model marks the safe and legal roads for regular driving in color blocks shaped by area.

논문

TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation

2403.16958 2024-03-25 이 페이지의 평가 4건

mIoU: 92.9Params (M): 1.94

HybridNets: End-to-End Perception Network

2203.09035 2022-03-17 이 페이지의 평가 1건

mIoU: 90.5Params (M): 12.8

TriLiteNet: Lightweight Model for Multi-Task Visual Perception

2025-03-17 이 페이지의 평가 1건

mIoU: 92.4Params (M): 2.35

TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

2307.10705 2023-07-20 이 페이지의 평가 1건

mIoU: 91.3Params (M): 0.43

YOLOP: You Only Look Once for Panoptic Driving Perception

2108.11250 2021-08-25 이 페이지의 평가 1건

mIoU: 91.5Params (M): 7.9

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

2208.11434 2022-08-24 이 페이지의 평가 1건

mIoU: 93.2Params (M): 38.9

You Only Look at Once for Real-time and Generic Multi-Task

2310.01641 2023-10-02 이 페이지의 평가 1건

mIoU: 91