Purpose: Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope. Design/methodology/approach: In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system. Findings: This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft. Originality/value: This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

The great development of robot vision represented by deep learning places urgent demands on embedded vision implementation. This article introduces a hardware framework for implementation of embedded vision based on digital signal processor, which can be widely used in robot vision applications. Firstly, the article discusses implementation of a pretrained typical convolutional neural network on the digital signal processor embedded system for real-time handwritten digit recognition. Then, the article introduces the migration of OpenCV software packages to digital signal processor embedded system and the implementation flow of face detection algorithms with OpenCV on digital signal processor. The experimental results are remarkable with convolutional neural networks for handwritten digit recognition. This article provides a convenient and feasible design scheme of digital signal processor system for implementation of embedded vision.