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.

翻译：实时感知（即流式感知）是自动驾驶的关键环节，但现有研究对此尚未深入探索。为弥补这一空白，我们提出DAMO-StreamNet——一个结合YOLO系列最新进展与时空感知机制综合分析而优化的框架，提供前沿解决方案。DAMO-StreamNet的核心创新包括：(1) 采用可变形卷积的鲁棒颈部结构，增强感受野与特征对齐能力；(2) 双分支结构融合短路径语义特征与长路径时序特征，提升运动状态预测精度；(3) 逻辑层蒸馏实现高效优化，在语义空间中对齐教师网络与学生网络的逻辑值；(4) 实时预测机制，以当前帧更新支持帧特征，确保推理过程中流式感知的无缝衔接。实验表明，DAMO-StreamNet超越现有最优方法，在未使用额外数据的情况下，标准尺寸（600, 960）下sAP达37.8%，大尺寸（1200, 1920）下达43.3%。该工作不仅为实时感知树立了新基准，也为未来研究提供了宝贵见解。此外，DAMO-StreamNet可应用于无人机、机器人等多种自主系统，为实时感知开辟道路。代码见https://github.com/zhiqic/DAMO-StreamNet。