Q-YOLOP: Quantization-Aware You only Look Once for Panoptic Driving Perception

Chi Chih Chang, Wei Cheng Lin, Pei Shuo Wang, Sheng Feng Yu, Yu Chen Lu, Kuan Cheng Lin, Kai Chiang Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work, we present an efficient and quantization-aware panoptic driving perception model (Q-YOLOP) for object detection, drivable area segmentation, and lane line segmentation, in the context of autonomous driving. Our model employs the Efficient Layer Aggregation Network (ELAN) as its backbone and task-specific heads for each task. We employ a four-stage training process that includes pretraining on the BDD100K dataset, finetuning on both the BDD100K and iVS datasets, and quantization-aware training (QAT) on BDD100K. During the training process, we use powerful data augmentation techniques, such as random perspective and mosaic, and train the model on a combination of the BDD100K and iVS datasets. Both strategies enhance the model's generalization capabilities. The proposed model achieves state-of-the-art performance with an mAP@0.5 of 0.622 for object detection and an mIoU of 0.612 for segmentation, while maintaining low computational and memory requirements.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-56
Number of pages5
ISBN (Electronic)9798350313154
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023 - Brisbane, Australia
Duration: 10 Jul 202314 Jul 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

Conference

Conference2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
Country/TerritoryAustralia
CityBrisbane
Period10/07/2314/07/23

Keywords

  • autonomous driving
  • Object detection
  • quantization-aware training
  • semantic segmentation

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