  The method of deep learning has achieved excellent results in improving the
performance of robotic grasping detection. However, the deep learning methods
used in general object detection are not suitable for robotic grasping
detection. Current modern object detectors are difficult to strike a balance
between high accuracy and fast inference speed. In this paper, we present an
efficient and robust fully convolutional neural network model to perform
robotic grasping pose estimation from an n-channel input image of the real
grasping scene. The proposed network is a lightweight generative architecture
for grasping detection in one stage. Specifically, a grasping representation
based on Gaussian kernel is introduced to encode training samples, which
embodies the principle of maximum central point grasping confidence. Meanwhile,
to extract multi-scale information and enhance the feature discriminability, a
receptive field block (RFB) is assembled to the bottleneck of our grasping
detection architecture. Besides, pixel attention and channel attention are
combined to automatically learn to focus on fusing context information of
varying shapes and sizes by suppressing the noise feature and highlighting the
grasping object feature. Extensive experiments on two public grasping datasets,
Cornell and Jacquard demonstrate the state-of-the-art performance of our method
in balancing accuracy and inference speed. The network is an order of magnitude
smaller than other excellent algorithms while achieving better performance with
an accuracy of 98.9$\%$ and 95.6$\%$ on the Cornell and Jacquard datasets,
respectively.
