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YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

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arxiv 2208.11434 v1 pith:RQWJ3P33 submitted 2022-08-24 cs.CV

Cheng Han , Qichao Zhao , Shuyi Zhang , Yinzi Chen , Zhenlin Zhang , Jinwei Yuan This is my paper · ORCID
classification cs.CV
keywords drivingachieveddetectionlearningmodelpanopticperceptionperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A single transformer-based network jointly performs object detection, drivable area segmentation, and lane line segmentation on BDD100K with reported state-of-the-art accuracy at real-time speed, along with a dilated ...

  2. Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Transfer-learned SpikeYOLO achieves mAP 0.937/0.771 and HOTA 0.701/0.445 on KITTI and BDD100K for two-class automotive detection and tracking, competitive with conventional deep networks.

  3. HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring

    cs.CV 2025-05 conditional novelty 4.0 of 10

    HMAD integrates BEVFormer, DiffusionDrive-style anchor offsets, and a Hydra-MDP-style scoring network to achieve 65.94 EPDMS on the NAVSIM warmup benchmark and 44.5% on the CVPR 2025 private test set.

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