  The vision-based grasp detection method is an important research direction in
the field of robotics. However, due to the rectangle metric of the grasp
detection rectangle's limitation, a false-positive grasp occurs, resulting in
the failure of the real-world robot grasp task. In this paper, we propose a
novel generative convolutional neural network model to improve the accuracy and
robustness of robot grasp detection in real-world scenes. First, a
Gaussian-based guided training method is used to encode the quality of the
grasp point and grasp angle in the grasp pose, highlighting the highest-quality
grasp point position and grasp angle and reducing the generation of
false-positive grasps. Simultaneously, deformable convolution is used to obtain
the shape features of the object in order to guide the subsequent network to
the position. Furthermore, a global-local feature fusion method is introduced
in order to efficiently obtain finer features during the feature reconstruction
stage, allowing the network to focus on the features of the grasped objects. On
the Cornell Grasping Datasets and Jacquard Datasets, our method achieves
excellent performance of 99.0$\%$ and 95.9$\%$, respectively. Finally, the
proposed method is put to the test in a real-world robot grasping scenario.
