论文标题
用于流知觉的实时对象检测
Real-time Object Detection for Streaming Perception
论文作者
论文摘要
自主驾驶要求模型感知环境，并在安全性较低的情况下行动。虽然过去的作品忽略了处理后不可避免的环境变化，但提出了流媒体感知，以将延迟和准确性共同评估为视频在线感知的单个指标。在本文中，我们指出的是，将实时模型预测未来的能力是解决此问题的关键，而不是像以前的作品那样搜索精度和速度之间的权衡。我们为流媒体感知建立了一个简单有效的框架。它具有新颖的双流感知模块（DFP），其中包括动态和静态流以捕获流媒体预测的运动趋势和基本检测功能。此外，我们引入了一种趋势感知损失（TAL），并结合了一个趋势因素，以生成具有不同移动速度的物体的自适应权重。我们的简单方法在Argoverse-HD数据集上实现了竞争性能，与强大的基线相比，AP提高了4.9％，从而验证了其有效性。我们的代码将在https://github.com/yancie-yjr/streamyolo上提供。
Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment after processing, streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem. We build a simple and effective framework for streaming perception. It equips a novel DualFlow Perception module (DFP), which includes dynamic and static flows to capture the moving trend and basic detection feature for streaming prediction. Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to generate adaptive weights for objects with different moving speeds. Our simple method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline, validating its effectiveness. Our code will be made available at https://github.com/yancie-yjr/StreamYOLO.
