  Grasp detection with consideration of the affiliations between grasps and
their owner in object overlapping scenes is a necessary and challenging task
for the practical use of the robotic grasping approach. In this paper, a
robotic grasp detection algorithm named ROI-GD is proposed to provide a
feasible solution to this problem based on Region of Interest (ROI), which is
the region proposal for objects. ROI-GD uses features from ROIs to detect
grasps instead of the whole scene. It has two stages: the first stage is to
provide ROIs in the input image and the second-stage is the grasp detector
based on ROI features. We also contribute a multi-object grasp dataset, which
is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation
Relationship Dataset. Experimental results demonstrate that ROI-GD performs
much better in object overlapping scenes and at the meantime, remains
comparable with state-of-the-art grasp detection algorithms on Cornell Grasp
Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can
help robots grasp the target in single-object and multi-object scenes with the
overall success rates of 92.5% and 83.8% respectively.
