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

Lin, Zhongyi; Yih, Matthew; Ota, Jeffrey M.; Owens, John D.; Muyan-Ozcelik, Pinar

doi:10.1109/tiv.2019.2919458

Cited by 24 publications

(9 citation statements)

References 10 publications

(17 reference statements)

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“…One of the most common DL models to detect traffic signs are CNNs. Qian et al (2015), Yang et al (2015), Lin et al (2016Lin et al ( , 2019, Lim et al (2017), Zeng et al (2016), Hu et al (2017b), Yuan et al (2016), Arcos-Garcia et al (2018), Natarajan et al (2018), Lee and Kim (2018), Li et al (2018b) and You et al (2018) have all used CNN as their main feature extractor, each trying to tune their model to get the best results. Qian et al (2015) have used RCNN to derive regions of interest from RGB images.…”

Section: Visual Recognition Tasksmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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Section: Visual Recognition Tasksmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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show abstract

“…However the comparison is not very fair: not all the networks are implemented on all the boards, the datasets are different and they do not even mention the input size of the networks. An extensive comparison is offered by Lin et al [20], where the authors benchmark several deep learning frameworks and investigate the FPGA deployment for performing traffic sign classification and detection. To evaluate inference performance, they consider inference latency, accuracy, and power efficiency, by varying different parameters such as floating-point precision and batch size.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Assessment of Embedded Neural Networks for Object Detection

Verucchi

Brilli

Sapienza

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Object detection is arguably one of the most important and complex tasks to enable the advent of next-generation autonomous systems. Recent advancements in deep learning techniques allowed a significant improvement in detection accuracy and latency of modern neural networks, allowing their adoption in automotive, avionics and industrial embedded systems, where performances are required to meet size, weight and power constraints. Multiple benchmarks and surveys exist to compare stateof-the-art detection networks, profiling important metrics, like precision, latency and power efficiency on Commercial-off-the-Shelf (COTS) embedded platforms. However, we observed a fundamental lack of fairness in the existing comparisons, with a number of implicit assumptions that may significantly bias the metrics of interest. This includes using heterogeneous settings for the input size, training dataset, threshold confidences, and, most importantly, platform-specific optimizations, that are especially important when assessing latency and energy-related values. The lack of uniform comparisons is mainly due to the significant effort required to re-implement network models, whenever openly available, on the specific platforms, to properly configure the available acceleration engines for optimizing performance, and to retrain the model using a homogeneous dataset. This paper aims at filling this gap, providing a comprehensive and fair comparison of the best-in-class Convolution Neural Networks (CNNs) for real-time embedded systems, detailing the effort made to achieve an unbiased characterization on cutting-edge system-on-chips. Multi-dimensional trade-offs are explored for achieving a proper configuration of the available programmable accelerators for neural inference, adopting the best available software libraries. To stimulate the adoption of fair benchmarking assessments, the framework is released to the public in an open source repository.

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“…In other plane, the usage of CNNs for medical research using OpenVINO in combination with the new Intel Xeon CPUs [28] , the efforts in deploying CNNs on FPGAs [29] or the work of Lin et al. [30] , studying OpenVINO and TensorFlow for deploying a traffic sign classification and detection system on an FPGA, are examples that prove the variety of scenarios in which this framework could be further used, besides embedded devices and IoT use cases.…”

Section: Related Workmentioning

confidence: 99%

CNN Inference acceleration using low-power devices for human monitoring and security scenarios

Mas

Panadero²,

Botella

et al. 2020

Computers & Electrical Engineering

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Security is currently one of the top concerns in our society. From governmental installations to private companies and medical institutions, they all have to address directly with security issues as: access to restricted information quarantine control, or criminal tracking. As an example, identifying patients is critical in hospitals or geriatrics in order to isolate infected people, which has proven to be a non- trivial issue with the COVID-19 pandemic that is currently affecting all countries, or to locate fled patients. Face recognition is then a non-intrusive alternative for performing these tasks. Although FaceNet from Google has proved to be almost perfect, in a multi-face scenario its performance decays rapidly. In order to mitigate this loss of performance, in this paper a cluster based on the Neural Computer Stick version 2 and OpenVINO by Intel is proposed. A detailed power and runtime study is shown for two programming models, namely: multithreading and multiprocessing. Furthermore, 3 different hosts have been considered. In the most efficient configuration, an average of 6 frames per second has been achieved using the Raspberry Pi 4 as host and with a power consumption of just 11.2W, increasing by a factor of 3.3X the energy efficiency with respect to a PC-based solution in a multi-face scenario.

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

Cited by 24 publications

References 10 publications

Applications of Deep Learning in Intelligent Transportation Systems

Applications of Deep Learning in Intelligent Transportation Systems

A Systematic Assessment of Embedded Neural Networks for Object Detection

CNN Inference acceleration using low-power devices for human monitoring and security scenarios

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