Real-time grasping detection method based on attention residual block and multi-scale receptive field

Abstract

In order to achieve high-precision and high-speed grasping detection, in this paper, we propose a lightweight grasping network model with the combination of attention residual block and multi-scale receptive field, which directly predicts the grasping pose of the manipulator from n-channel grasping scene images. The proposed grasping network structure extracts features from the input image based on the residual block introduced with the attention mechanism, and combines the receptive field module to improve the discriminability of the multi-scale features of the image. The network model is trained and verified on the Cornell grasping dataset. The results show that the multi-modal image information can improve the accuracy of grasping detection. With RGB-D image as input, the grasping accuracy rate of both image-wise and object-wise can reach 97.7%, which is better than other current grasping detection algorithms. Meanwhile, the detection speed of the algorithm is 17ms, which ensures the real-time performance of grasping detection tasks.