关键词: 深度学习, DenseNet, SqueezeNet, 机器人抓取检测, 轻量级卷积神经网络

Abstract:

Convolutional neural network has achieved good detection performances on vision-based robotic grasp detection. However, most of them have too many calculation parameters and are not suitable for the system with limited resources. To solve this problem, a lightweight grasp detection regression model SqueezeNet-RM（SqueezeNet Regression Model） is proposed based on the lightweight neural network SqueezeNet and the idea of using multiple bypass connections to enhance feature reuse in DenseNet, and SqueezeNet-RM is used to extract multi-modal features（RGD） from RGB-D images to predict the best grasp pose for two-finger robotic gripper. On the standard Cornell Grasp Dataset, compared with the classical grasp detection method, the proposed lightweight grasp detection network takes up less storage space and shows faster detection speed and higher generalization performance without decreasing the detection accuracy. The storage space occupied by the proposed model is 86.97% smaller than that of AlexNet and 3 times faster, which is more feasible to deploy on FPGA（Field Programmable Gate Array） or resource restricted robotic grasp detection system.

Key words: deep learning, DenseNet, SqueezeNet, robotic grasp detection, lightweight convolutional neural network