Expandable YOLO: 3D Object Detection from RGB-D Images
Masahiro Takahashi, Yonghoon Ji, Kazunori Umeda, Alessandro Moro
Abstract
This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. In addition, Intersection over Union (IoU) in 3D space is introduced to confirm the accuracy of region extraction results. In the field of deep learning, object detectors that use distance information as input are actively studied for utilizing automated driving. However, the conventional detector has a large network structure, and the real-time property is impaired. The effectiveness of the detector constructed as described above is verified using datasets. The experiment verified that the proposed model is able to output 3D bounding boxes and detect people whose body is partly hidden. Further, the processing speed of the model reached 44.35 fps.