Drivable Area Detection
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Benchmarks
BDD100K val
Most implemented
YOLOP: You Only Look Once for Panoptic Driving Perception
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
HybridNets: End-to-End Perception Network
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Papers
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To addre…
Autonomous DrivingComputational EfficiencyDrivable Area DetectionLane Detection+4Task-Oriented Pre-Training for Drivable Area Detection
Pre-training techniques play a crucial role in deep learning, enhancing models' performance across a variety of tasks. By initially training on large datasets and subsequently fine-tuning on task-specific data, pre-train…
Drivable Area DetectionTwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
Semantic segmentation is crucial for autonomous driving, particularly for Drivable Area and Lane Segmentation, ensuring safety and navigation. To address the high computational costs of current state-of-the-art (SOTA) mo…
Autonomous DrivingDrivable Area DetectionLane DetectionSegmentation+1TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions
Detection of the drivable area in all conditions is crucial for autonomous driving and advanced driver assistance systems. However, the amount of labeled data in adverse driving conditions is limited, especially in winte…
Autonomous DrivingDrivable Area DetectionYou Only Look at Once for Real-time and Generic Multi-Task
High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task mo…
Autonomous DrivingDrivable Area DetectionLane Detectionobject-detection+3TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the r…
Autonomous DrivingAutonomous VehiclesDrivable Area DetectionGPU+4