论文标题
深度估计是否有助于对象检测？
Does depth estimation help object detection?
论文作者
论文摘要
与颜色数据结合使用，地面真相深度有助于提高仅使用颜色的基线模型的对象检测准确性。但是，估计的深度并不总是会改善。当使用估计深度时，许多因素会影响对象检测的性能。在本文中，我们通过详细的实验全面研究了这些因素，例如使用地面真相与估计的深度，不同最先进的深度估计网络的影响，使用不同的室内和室外RGB-D数据集作为深度估计的训练数据以及将深度集成到基础对象对象探测器网络的影响。我们提出了一种早期的深度串联策略，该策略的图比以前的作品更高，同时使用明显更少的参数。
Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield improvements. Many factors affect the performance of object detection when estimated depth is used. In this paper, we comprehensively investigate these factors with detailed experiments, such as using ground-truth vs. estimated depth, effects of different state-of-the-art depth estimation networks, effects of using different indoor and outdoor RGB-D datasets as training data for depth estimation, and different architectural choices for integrating depth to the base object detector network. We propose an early concatenation strategy of depth, which yields higher mAP than previous works' while using significantly fewer parameters.
