  In this paper, we focus on the question: how might mobile robots take
advantage of affordable RGB-D sensors for object detection? Although current
CNN-based object detectors have achieved impressive results, there are three
main drawbacks for practical usage on mobile robots: 1) It is hard and
time-consuming to collect and annotate large-scale training sets. 2) It usually
needs a long training time. 3) CNN-based object detection shows significant
weakness in predicting location. We propose an improved method for the
detection of planar objects, which rectifies images with geometric information
to compensate for the perspective distortion before feeding it to the CNN
detector module, typically a CNN-based detector like YOLO or MASK RCNN. By
dealing with the perspective distortion in advance, we eliminate the need for
the CNN detector to learn that. Experiments show that this approach
significantly boosts the detection performance. Besides, it effectively reduces
the number of training images required. In addition to the novel detection
framework proposed, we also release an RGBD dataset and source code for hazmat
sign detection. To the best of our knowledge, this is the first work of image
rectification for CNN-based object detection, and the dataset is the first
public available hazmat sign detection dataset with RGB-D sensors.
