Abstract
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.
Highlights
Today, artificial intelligence has been widely applied in the field of computer science.[1,2] As we all know, machine learning is an important part of artificial intelligence, which is regarded as a new technology that would be integrated into the embedded system
Embedded system based on digital signal processor (DSP) or advanced RISC machine (ARM) is a technology development direction, which has been widely recognized in computer, communication, and information industries with its powerful and flexible applicability.[3,4]
The hardware framework of the system is mainly based on the peripherals video port interface (VPIF), liquid crystal display controller (LCDC), general-purpose input output (GPIO), and external memory interface A (EMIFA) of TMS320C6748 to interface with the camera module ATK-OV5640, liquid crystal display module (LCDM), keys, and flash memory, respectively
Summary
Artificial intelligence has been widely applied in the field of computer science.[1,2] As we all know, machine learning is an important part of artificial intelligence, which is regarded as a new technology that would be integrated into the embedded system. At present, embedded/robot vision applications are mainly implemented on hardware platforms such as DSP, field-programmable gate array (FPGA), and ARM. We introduce a hardware framework for implementation of embedded vision based on TMS320C6748. TMS320C6748 is a fixed-point and floating-point DSP based on a C674x DSP core by TI This article adopts this framework for implementation convolutional neural network (CNN) on DSP embedded system. The hardware architecture of the system mainly incorporates the peripherals of TMS320C6748, a liquid crystal display module (LCDM) and an image sensor module It utilizes the powerful digital signal processing capability of TMS320C6748 to implement a pretrained CNN, which is trained on PC based on TensorFlow beforehand. This article provides a convenient and feasible design scheme of the DSP application system for implementation of embedded vision algorithm. The fifth section draws the conclusion remarks and discusses our future work
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