SOTAVerified

Drivable Area Detection

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 12 papers

TitleStatusHype
TriLiteNet: Lightweight Model for Multi-Task Visual PerceptionCode1
Task-Oriented Pre-Training for Drivable Area Detection—0
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane SegmentationCode2
TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions—0
You Only Look at Once for Real-time and Generic Multi-TaskCode2
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving CarsCode1
YOLOPv2: Better, Faster, Stronger for Panoptic Driving PerceptionCode2
HybridNets: End-to-End Perception NetworkCode2
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images—0
YOLOP: You Only Look Once for Panoptic Driving PerceptionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1YOLOPv2mIoU93.2—Unverified
2TwinLiteNetPlus-LargemIoU92.9—Unverified
3TriLiteNet-basemIoU92.4—Unverified
4TwinLiteNetPlus-MediummIoU92—Unverified
5YOLOPmIoU91.5—Unverified
6TwinLiteNetmIoU91.3—Unverified
7A-YOLOM(s)mIoU91—Unverified
8TwinLiteNetPlus-SmallmIoU90.6—Unverified
9HybridNetsmIoU90.5—Unverified
10TwinLiteNetPlus-NanomIoU87.3—Unverified