mlsanityData to 7 Oct 2026

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

arXiv 2208.11434 ↗First posted 24 Aug 2022cs.CVcited by 25

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Abstract (arXiv)

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. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper proposed an effective and efficient multi-task learning network to simultaneously perform the task of traffic object detection, drivable road area segmentation and lane detection. Our model achieved the new state-of-the-art (SOTA) performance in terms of accuracy and speed on the challenging BDD100K dataset. Especially, the inference time is reduced by half compared to the previous SOTA model. Code will be released in the near future.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 26 of its 30 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

26 of 30 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2018YOLOv3: An Incremental Improvementcited by 2,652
  7. 2016Pyramid Scene Parsing Networkcited by 2,472
  8. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  10. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  11. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  12. 2018Path Aggregation Network for Instance Segmentationcited by 796
14 more
  1. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  2. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  3. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  4. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 331
  5. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  6. 2019Learning Lightweight Lane Detection CNNs by Self Attention Distillationcited by 162
  7. 2017SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networkscited by 132
  8. 2016MultiNet: Real-time Joint Semantic Reasoning for Autonomous Drivingcited by 90
  9. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
  10. 2021YOLOP: You Only Look Once for Panoptic Driving Perceptioncited by 82
  11. 2018AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomycited by 54
  12. 2018LaneNet: Real-Time Lane Detection Networks for Autonomous Drivingcited by 39
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  14. 2021Location-Sensitive Visual Recognition with Cross-IOU Losscited by 8

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Citing papers per year: 2023: 7; 2024: 9; 2025: 6; 2026: 3.

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  1. 2024RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Modelscited by 20
  2. 2023TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Carscited by 13
  3. 2023ASY-VRNet: Waterway Panoptic Driving Perception Model based on Asymmetric Fair Fusion of Vision and 4D mmWave Radarcited by 11
  4. 2024DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactionscited by 8
  5. 2023Multi-Modal Multi-Task (3MT) Road Segmentationcited by 6
  6. 2024YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systemscited by 6
  7. 2024TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Drivingcited by 5
  8. 2023Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalitiescited by 4
  9. 2024Panoptic Perception for Autonomous Driving: A Surveycited by 4
  10. 2024Monocular Lane Detection Based on Deep Learning: A Surveycited by 4
  11. 2025MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detectioncited by 4
  12. 2023Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Modelcited by 2
13 more
  1. 2025RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Drivingcited by 2
  2. 2025TriLiteNet: Lightweight Model for Multi-Task Visual Perceptioncited by 2
  3. 2026UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perceptioncited by 2
  4. 2023Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Predictioncited by 1
  5. 2024Vision-based Wearable Steering Assistance for People with Impaired Vision in Joggingcited by 1
  6. 2025HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoringcited by 1
  7. 2023Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perceptioncited by 0
  8. 2024LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systemscited by 0
  9. 2024An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Drivingcited by 0
  10. 2025FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfercited by 0
  11. 2025A Survey on Deep Multi-Task Learning in Connected Autonomous Vehiclescited by 0
  12. 2026Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computingcited by 0
  13. 2026An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perceptioncited by 0