  The reliability of grasp detection for target objects in complex scenes is a
challenging task and a critical problem that needs to be solved urgently in
practical application. At present, the grasp detection location comes from
searching the feature space of the whole image. However, the cluttered
background information in the image impairs the accuracy of grasping detection.
In this paper, a robotic grasp detection algorithm named MASK-GD is proposed,
which provides a feasible solution to this problem. MASK is a segmented image
that only contains the pixels of the target object. MASK-GD for grasp detection
only uses MASK features rather than the features of the entire image in the
scene. It has two stages: the first stage is to provide the MASK of the target
object as the input image, and the second stage is a grasp detector based on
the MASK feature. Experimental results demonstrate that MASK-GD's performance
is comparable with state-of-the-art grasp detection algorithms on Cornell
Datasets and Jacquard Dataset. In the meantime, MASK-GD performs much better in
complex scenes.
