论文标题
DADE：流媒体感知的延迟自适应检测器
DaDe: Delay-adaptive Detector for Streaming Perception
论文作者
论文摘要
在低潜伏期中识别周围环境对于自动驾驶至关重要。在实时环境中，处理结束后，周围环境会发生变化。当前的检测模型无法处理处理后发生的环境变化。提出了流媒体感知来评估实时视频感知的延迟和准确性。但是，由于硬件资源有限，高温和其他因素，在现实世界应用中出现了其他问题。在这项研究中，我们开发了一个模型，该模型可以实时反映处理延迟并产生最合理的结果。通过合并提出的功能队列和特征选择模块，系统可以获得预测特定时间步长的能力，而无需任何额外的计算成本。我们的方法在Argoverse-HD数据集上进行了测试。在延迟时，在各种环境中，它的性能比当前的最新方法（2022.12）更高。该代码可从https://github.com/danjos95/dade获得
Recognizing the surrounding environment at low latency is critical in autonomous driving. In real-time environment, surrounding environment changes when processing is over. Current detection models are incapable of dealing with changes in the environment that occur after processing. Streaming perception is proposed to assess the latency and accuracy of real-time video perception. However, additional problems arise in real-world applications due to limited hardware resources, high temperatures, and other factors. In this study, we develop a model that can reflect processing delays in real time and produce the most reasonable results. By incorporating the proposed feature queue and feature select module, the system gains the ability to forecast specific time steps without any additional computational costs. Our method is tested on the Argoverse-HD dataset. It achieves higher performance than the current state-of-the-art methods(2022.12) in various environments when delayed . The code is available at https://github.com/danjos95/DADE
