论文标题
透视意识到的道路障碍物检测
Perspective Aware Road Obstacle Detection
论文作者
论文摘要
尽管道路障碍检测技术变得越来越有效，但它们通常忽略这样一个事实，即在实践中，障碍物的明显大小随着车辆的距离的增加而减小。在本文中，我们通过计算一个刻度映射来解释这一点，该比例图编码每个图像位置的假设对象的明显大小。然后，我们将此视角映射利用（i）通过注入道路合成对象来生成训练数据，其大小对应于透视图预先构成； （ii）将透视信息纳入检测网络的解码部分，以指导障碍物检测器。我们对标准基准测试的结果表明，这两种策略共同提高了障碍检测性能，从而使我们的方法在实例级别的障碍物检测方面始终超过了最先进的方法。
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 onto the road synthetic objects whose size corresponds to the perspective foreshortening; 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.
