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.

翻译：尽管道路障碍物检测技术已日益有效，但通常忽略了实际场景中障碍物在图像上的表观尺寸会随其与车辆距离增加而减小的事实。本文通过计算包含假设物体在图像各位置表观尺寸信息的尺度图来解决该问题。我们利用该透视映射图实现以下目标：（i）通过在道路区域注入尺寸符合透视缩短效应的合成物体生成训练数据；（ii）在检测网络的解码部分融入透视信息以引导障碍物检测器。标准基准测试结果表明，这两种策略的协同作用显著提升了障碍物检测性能，使我们的方法在实例级障碍物检测任务中持续超越现有最先进方法。