Near Real-Time Object Recognition for Pepper based on Deep Neural Networks Running on a Backpack
This provides a practical solution for robotics applications requiring real-time object detection, though it is incremental as it adapts existing methods to hardware constraints.
The paper tackled the problem of enabling Pepper robot to perform near real-time object recognition by implementing a YOLO-based deep neural network on an external Jetson TK1 card in a backpack, achieving detection at about 5 frames per second on 320x320 pixel images.
The main goal of the paper is to provide Pepper with a near real-time object recognition system based on deep neural networks. The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed. In addition, considering that YOLO cannot be run in the Pepper's internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery. By using this card, Pepper is able to robustly detect and recognize objects in images of 320x320 pixels at about 5 frames per second.