# YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

arXiv: https://arxiv.org/abs/2208.11434 · HTML: https://mlsanity.com/paper/2208.11434/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks](https://mlsanity.com/paper/1708.04485/) (2017)
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://mlsanity.com/paper/1612.07695/) (2016)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [YOLOP: You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2108.11250/) (2021)
- [AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy](https://mlsanity.com/paper/1808.05238/) (2018)
- [LaneNet: Real-Time Lane Detection Networks for Autonomous Driving](https://mlsanity.com/paper/1807.01726/) (2018)
- [HybridNets: End-to-End Perception Network](https://mlsanity.com/paper/2203.09035/) (2022)
- [Location-Sensitive Visual Recognition with Cross-IOU Loss](https://mlsanity.com/paper/2104.04899/) (2021)

## Built on by (most cited first, 26 of 26)

- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)
- [TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars](https://mlsanity.com/paper/2307.10705/) (2023)
- [ASY-VRNet: Waterway Panoptic Driving Perception Model based on Asymmetric Fair Fusion of Vision and 4D mmWave Radar](https://mlsanity.com/paper/2308.10287/) (2023)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [Multi-Modal Multi-Task (3MT) Road Segmentation](https://mlsanity.com/paper/2308.11983/) (2023)
- [TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving](https://mlsanity.com/paper/2403.16958/) (2024)
- [Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities](https://mlsanity.com/paper/2312.08851/) (2023)
- [Panoptic Perception for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.15388/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection](https://mlsanity.com/paper/2504.06801/) (2025)
- [Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model](https://mlsanity.com/paper/2310.16717/) (2023)
- [RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving](https://mlsanity.com/paper/2508.06529/) (2025)
- [TriLiteNet: Lightweight Model for Multi-Task Visual Perception](https://mlsanity.com/paper/2509.04092/) (2025)
- [UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception](https://mlsanity.com/paper/2602.01594/) (2026)
- [Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction](https://mlsanity.com/paper/2312.02872/) (2023)
- [Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging](https://mlsanity.com/paper/2408.00332/) (2024)
- [LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems](https://mlsanity.com/paper/2408.09117/) (2024)
- [HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring](https://mlsanity.com/paper/2505.23129/) (2025)
- [Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2307.04537/) (2023)
- [An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2409.10554/) (2024)
- [FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfer](https://mlsanity.com/paper/2505.11897/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Beyond Scanpaths: Graph-Based Gaze Simulation in Dynamic Scenes](https://mlsanity.com/paper/2603.28319/) (2026)
- [Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing](https://mlsanity.com/paper/2607.04921/) (2026)
- [An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception](https://mlsanity.com/paper/2609.05516/) (2026)

## 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.