  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 Uninon (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. As a result of this experiment, the proposed model is able to
output 3D bounding boxes and detect people whose part of the body is hidden.
Further, the processing speed of the model is 44.35 fps.
