计算机科学
现场可编程门阵列
目标检测
深度学习
人工智能
卷积神经网络
视觉对象识别的认知神经科学
库达
绘图
图形处理单元
协处理器
通用串口总线
领域(数学)
对象(语法)
嵌入式系统
计算机视觉
模式识别(心理学)
计算机图形学(图像)
并行计算
软件
程序设计语言
纯数学
数学
作者
Veysel Yusuf ÇAMBAY,Ayşegül Uçar,Muhammet Ali Arseri̇m
出处
期刊:2019 International Artificial Intelligence and Data Processing Symposium (IDAP)
日期:2019-09-01
被引量:7
标识
DOI:10.1109/idap.2019.8875870
摘要
Object detection and recognition is one of the main tasks in many areas such as autonomous unmanned ground vehicles, robotic and medical image processing. Recently, deep learning has been used by many researchers in these areas when the data measure is large. In particular, one of the most up-to-date structures of deep learning, Convolutional Neural Networks (CNNs) has achieved great success in this field. Real-time works related to CNNs are carried out by using GPU-Graphics Processing Units. Although GPUs provides high stability, they requires high power, energy consumption, and large computational load problems. In order to overcome this problem, it has started to used the Field Programmable Gate Arrays (FPGAs). In this article, object detection and recognition procedures were performed using the ZYNQ XC7Z020 development board including both the ARM processor and the FPGA. Real-time object recognition has been made with the Movidius USB-GPU externally plugged into the FPGA. The results are given with figures.
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