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
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.