  Nowadays, plenty of deep learning technologies are being applied to all
aspects of autonomous driving with promising results. Among them, object
detection is the key to improve the ability of an autonomous agent to perceive
its environment so that it can (re)act. However, previous vision-based object
detectors cannot achieve satisfactory performance under real-time driving
scenarios. To remedy this, we present the real-time steaming perception system
in this paper, which is also the 2nd Place solution of Streaming Perception
Challenge (Workshop on Autonomous Driving at CVPR 2021) for the detection-only
track. Unlike traditional object detection challenges, which focus mainly on
the absolute performance, streaming perception task requires achieving a
balance of accuracy and latency, which is crucial for real-time autonomous
driving. We adopt YOLOv5 as our basic framework, data augmentation,
Bag-of-Freebies, and Transformer are adopted to improve streaming object
detection performance with negligible extra inference cost. On the Argoverse-HD
test set, our method achieves 33.2 streaming AP (34.6 streaming AP verified by
the organizer) under the required hardware. Its performance significantly
surpasses the fixed baseline of 13.6 (host team), demonstrating the
potentiality of application.
