论文标题
车道检测算法的硬件加速度：GPU与FPGA比较
Hardware Acceleration of Lane Detection Algorithm: A GPU Versus FPGA Comparison
论文作者
论文摘要
完整的计算机视觉系统可以分为两个主要类别：检测和分类。车道检测算法是计算机视觉检测类别的一部分，已应用于自动驾驶和智能车辆系统。车道检测系统负责在复杂的道路环境中进行车道标记。同时，巷检测在出发车道时在汽车的警告系统中起着至关重要的作用。实施的车道检测算法主要分为两个步骤：边缘检测和线检测。在本文中，我们将比较与FPGA和GPU获得的最先进的实施绩效，以评估折衷的潜伏期，功耗和利用率。我们的比较强调了这两个系统的优势和缺点。
A Complete Computer vision system can be divided into two main categories: detection and classification. The Lane detection algorithm is a part of the computer vision detection category and has been applied in autonomous driving and smart vehicle systems. The lane detection system is responsible for lane marking in a complex road environment. At the same time, lane detection plays a crucial role in the warning system for a car when departs the lane. The implemented lane detection algorithm is mainly divided into two steps: edge detection and line detection. In this paper, we will compare the state-of-the-art implementation performance obtained with both FPGA and GPU to evaluate the trade-off for latency, power consumption, and utilization. Our comparison emphasises the advantages and disadvantages of the two systems.
