Hardware-Oriented Dual Stream Object Recognition System using Binarized Neural Networks

Abstract

Service robots require an object recognition system to ensure their effective functioning in situations where Convolutional Neural Networks (CNN) are the mainstream machine learning technique employed by the system. Particularly, “Dual Stream VGG-16 (DS-VGG16),” which uses RGB and depth images, has been reported to have high accuracy for object recognition. However, it is difficult to implement CNN in robots, because it requires high computation power and consumes a huge amount of power. Although, implementing CNN with Field Programmable Gate Array (FPGA) solves the electric power problem, however it is difficult due to limited resources available. This paper proposes “Binarized Dual Stream VGG-16 (BDS-VGG16),” which is Hardware-Oriented DS-VGG16. With the concept of Binarized Neural Networks (BNN), BDS-VGG16 is effective when implemented on FPGA. In results, the accuracy of BDS-VGG16 is 99.2%. It is higher than that of Eitel's model by 5.1 points. Further, we developed an object recognition system based on the Robot Operating System (ROS) which is a well-known middleware for robots, it uses our proposed method for service robot application.