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Drivable Area Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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
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Task-Oriented Pre-Training for Drivable Area Detection
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions
You Only Look at Once for Real-time and Generic Multi-Task
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
HybridNets: End-to-End Perception Network
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
YOLOP: You Only Look Once for Panoptic Driving Perception
Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.