Benchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection

Zhongyi Lin; Matthew Yih; Jeffrey M. Ota; John D. Owens; Pınar Muyan-Özçelik

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Benchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection

机译：对深度学习框架进行基准测试并研究FPGA的部署，以进行交通标志分类和检测

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摘要

We benchmark several widely-used deep learning frameworks and investigate the field programmable gate array (FPGA) deployment for performing traffic sign classification and detection. We evaluate the training speed and inference accuracy of these framewor

机译：我们对几种广泛使用的深度学习框架进行了基准测试，并研究了现场可编程门阵列（FPGA）部署以执行交通标志分类和检测。我们评估这些构架的训练速度和推理准确性

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来源
《IEEE Transactions on Intelligent Vehicles》 |2019年第3期|385-395|共11页
作者
Zhongyi Lin; Matthew Yih; Jeffrey M. Ota; John D. Owens; Pınar Muyan-Özçelik;
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作者单位

Department of Electrical and Computer Engineering University of California Davis CA USA;

Autonomous Driving and Sports Research Group Intel Labs Santa Clara;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Field programmable gate arrays; Training; Benchmark testing; Graphics processing units; Deep learning; Computational modeling; Task analysis;

机译：现场可编程门阵列;训练;基准测试;图形处理单元;深度学习;计算建模;任务分析;

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1. Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving [J] . Munoz-Organero Mario, Ruiz-Blaquez Ramona, Sanchez-Fernandez Luis Computers，environment and urban systems . 2018,第mara期

机译：自动检测交通信号灯，街道交叉口和城市回旋处，结合异常检测和基于GPS行驶时的深度学习分类技术
2. Traffic signs classification by deep learning for advanced driving assistance systems [J] . Farag Wael Intelligent decision technologies . 2019,第3期

机译：通过深度学习对交通标志进行分类，以实现高级驾驶辅助系统
3. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning [J] . Khan SanaUllah, Islam Naveed, Jan Zahoor, Pattern recognition letters . 2019,第JULa期

机译：一种新颖的基于深度学习的框架，通过转移学习对乳腺癌进行检测和分类
4. Benchmarking Deep Learning Frameworks with FPGA-suitable Models on a Traffic Sign Dataset [C] . Zhongyi Lin, Jeffrey M. Ota, John D. Owens, IEEE Intelligent Vehicles Symposium . 2018

机译：在交通标志数据集上使用适合FPGA的模型对深度学习框架进行基准测试
5. Deep Learning Using Computer Vision in Self Driving Cars for Lane and Traffic Sign Detection [D] . Kanagaraj, Nitin. 2019

机译：深入学习在自动驾驶汽车驾驶车道和交通标志检测中的深度学习
6. An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions [O] . Mengsu Xiao, Chenyang Zhao, Qingli Zhu, 2019

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7. $Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework [O] . Yi Zeng, Huaxi Gu, Wenting Wei, 2019

机译：$深满$：基于深度学习的网络加密流量分类和入侵检测框架

Benchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection

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