Research on Robotic Grasping Detection Based on Improved Cascade R-CNN Model

doi:10.12068/j.issn.1005-3026.2023.06.006

Abstract

Abstract: In order to improve the grasping detection accuracy of the multi-object grasping detection network， a robotic arm grasping detection algorithm based on the improved Cascade R-CNN model is proposed. Firstly， the introduction of the ResNeXt structure can improve the accuracy of the model without increasing the difficulty of network design. The atrous spatial pyramid pooling module is introduced to solve the problem of low resolution. Then， the grasping box regression branch and the angle classification branch are optimized by the divide and conquer method. Secondly， aiming at the lack of multi-object grasping datasets， a multi-object grasping dataset (MOGD) is constructed， which effectively expands the multi-object grasping detection dataset. Finally， a grasping detection network is designed based on the improved Cascade R-CNN model. The experimental results show that the improved algorithm is more efficient. The experimental accuracy of the PI-Cascade R-CNN is 93 %， which is 1.5 percentage higher than that of the Cascade R-CNN.

Key words: grasping detection; atrous convolution; Cascade R-CNN; multi-object detection; robotic grasping

CLC Number:

TP242.6

JIANG Yang， ZHAO Feng-yu， CHEN Xiao. Research on Robotic Grasping Detection Based on Improved Cascade R-CNN Model[J]. Journal of Northeastern University(Natural Science), 2023, 44(6): 799-807.

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