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

TL;DR
This paper introduces Q-YOLOP, a quantization-aware, efficient panoptic perception model for autonomous driving that combines multiple perception tasks with state-of-the-art accuracy and low resource consumption.
Contribution
The paper presents a novel quantization-aware training approach for a multi-task panoptic perception model using ELAN backbone, achieving high accuracy with reduced computational costs.
Findings
Achieves [email protected] of 0.622 for object detection
Achieves mIoU of 0.612 for segmentation
Maintains low computational and memory requirements
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 [email protected] of 0.622 for object detection and an mIoU of…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
