单目摄像头实时感知系统，速度提升5.5倍且精度高
Single-Eye View: Monocular Real-time Perception Package for Autonomous Driving
- 端到端学习结合局部建图，统一处理目标检测与深度估计
- 在单块GPU上实现29帧/秒，比最快方法快555%
- 适合需要低延迟的自动驾驶感知系统开发
随着基于摄像头的自动驾驶技术快速发展，性能常被优先考虑，而计算效率却常被忽视。本文提出LRHPerception，一种面向自动驾驶的单目实时感知系统，利用单视角摄像头视频理解周围环境。该系统融合端到端学习的高效性与局部建图方法的丰富表征能力，在统一框架中集成目标跟踪与预测、道路分割和深度估计。将单目图像处理为包含RGB、道路分割和像素级深度估计的五通道张量，并附加目标检测与轨迹预测结果。实验表明，系统在单块GPU上实现29 FPS的实时处理速度，相比最快基于建图的方法提速555%。
原文摘要 · Abstract (English)
Amidst the rapid advancement of camera-based autonomous driving technology, effectiveness is often prioritized with limited attention to computational efficiency. To address this issue, this paper introduces LRHPerception, a real-time monocular perception package for autonomous driving that uses single-view camera video to interpret the surrounding environment. The proposed system combines the computational efficiency of end-to-end learning with the rich representational detail of local mapping methodologies. With significant improvements in object tracking and prediction, road segmentation, and depth estimation integrated into a unified framework, LRHPerception processes monocular image data into a five-channel tensor consisting of RGB, road segmentation, and pixel-level depth estimation, augmented with object detection and trajectory prediction. Experimental results demonstrate strong performance, achieving real-time processing at 29 FPS on a single GPU, representing a 555% speedup over the fastest mapping-based approach.
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