2 research outputs found
Improving CNN-based Planar Object Detection with Geometric Prior Knowledge
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.Comment: Accepted for SSRR 2020 (IEEE International Symposium on Safety,
Security, and Rescue Robotics
Tiny-YOLO object detection supplemented with geometrical data
We propose a method of improving detection precision (mAP) with the help of
the prior knowledge about the scene geometry: we assume the scene to be a plane
with objects placed on it. We focus our attention on autonomous robots, so
given the robot's dimensions and the inclination angles of the camera, it is
possible to predict the spatial scale for each pixel of the input frame. With
slightly modified YOLOv3-tiny we demonstrate that the detection supplemented by
the scale channel, further referred as S, outperforms standard RGB-based
detection with small computational overhead.Comment: 5 pages, 5 figures, published in 2020 IEEE 91st Vehicular Technology
Conference (VTC2020-Spring