Perspective Aware Road Obstacle Detection
视角感知道路障碍物检测
来自arXiv 2022-10-06 01:41:32
While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this paper, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) generate training data by injecting synthetic objects onto the road in a more realistic fashion than existing methods; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.
虽然道路障碍物检测技术变得越来越有效, 他们通常忽略了这样一个事实,即在实践中, 障碍物与车辆的距离越远,障碍物就越少。在这篇论文中, 我们通过计算比例尺地图来说明这一点,该地图编码了 每个图像位置都有假想对象。然后我们利用这一观点 地图以(I)通过将合成对象注入道路来生成训练数据 以比现有方法更切合实际的方式;和(2)纳入 透视信息在侦测网络的解码部分进行引导 障碍物探测器。我们对标准基准的结果表明,总的来说, 这两种策略显著提高了障碍物检测性能, 使我们的方法始终优于最先进的方法 实例级障碍物检测术语。
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