  Real-time perception, or streaming perception, is a crucial aspect of
autonomous driving that has yet to be thoroughly explored in existing research.
To address this gap, we present DAMO-StreamNet, an optimized framework that
combines recent advances from the YOLO series with a comprehensive analysis of
spatial and temporal perception mechanisms, delivering a cutting-edge solution.
The key innovations of DAMO-StreamNet are (1) A robust neck structure
incorporating deformable convolution, enhancing the receptive field and feature
alignment capabilities (2) A dual-branch structure that integrates short-path
semantic features and long-path temporal features, improving motion state
prediction accuracy. (3) Logits-level distillation for efficient optimization,
aligning the logits of teacher and student networks in semantic space. (4) A
real-time forecasting mechanism that updates support frame features with the
current frame, ensuring seamless streaming perception during inference. Our
experiments demonstrate that DAMO-StreamNet surpasses existing state-of-the-art
methods, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200,
1920)) sAP without using extra data. This work not only sets a new benchmark
for real-time perception but also provides valuable insights for future
research. Additionally, DAMO-StreamNet can be applied to various autonomous
systems, such as drones and robots, paving the way for real-time perception.
The code is at https://github.com/zhiqic/DAMO-StreamNet.
