A scalable FPGA based accelerator for Tiny-YOLO-v2 using OpenCL

Deep Convolution Neural Network (CNN) algorithm have recently gained popularity in many applications such as image classification, video analytic, object recognition and segmentation. Being compute-intensive and memory expensive, CNN computations are common accelerated by GPUs with high power dissipations. Recent studies show implementation of CNN on FPGA and it gain higher advantage in term of energy-efficient and flexibility over Software-configurable-GPUs. The proposed framework is verified by implement Tiny-YOLO-v2 on De1SoC. The design development in this project is HLS approach to ease effort from writing complex RTL codes and provide fast verification through emulation and profiling tools provided in the OpenCL SDK. To best of our knowledge, this is the first implementation of Tiny-YOLO-v2 CNN based object detection algorithm on a small scale De1SoC board using Intel FPGA SDK for OpenCL approach.

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Image Classification and Object Detection Algorithm Based on Convolutional Neural Network

Science Insights ◽

10.15354/si.19.re117 ◽

2019 ◽

Vol 31 (1) ◽

pp. 85-100

Author(s):

Juan K. Leonard ◽

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

Detection Algorithm

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Detection of sharp objects using deep neural network based object detection algorithm

2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP) ◽

10.1109/icccsp49186.2020.9315272 ◽

2020 ◽

Author(s):

R Kayalvizhi ◽

S Malarvizhi ◽

Siddhartha Dhar Choudhury ◽

Anita Topkar ◽

P Vijayakumar

Keyword(s):

Neural Network ◽

Object Detection ◽

Deep Neural Network ◽

Detection Algorithm

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Deep Convolution Neural Network with 2-Stage Transfer Learning for Medical Image Classification

The Brain & Neural Networks ◽

10.3902/jnns.24.3 ◽

2017 ◽

Vol 24 (1) ◽

pp. 3-12 ◽

Cited By ~ 1

Author(s):

Hayaru Shouno ◽

Aiga Suzuki ◽

Satoshi Suzuki ◽

Shoji Kido

Keyword(s):

Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Medical Image ◽

Convolution Neural Network ◽

Deep Convolution Neural Network ◽

Medical Image Classification

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Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies ◽

10.1115/smasis2018-8226 ◽

2018 ◽

Author(s):

Hongguo Su ◽

Mingyuan Zhang ◽

Shengyuan Li ◽

Xuefeng Zhao

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

State Of The Art ◽

Spatial Location ◽

Average Precision ◽

Practical Applications ◽

Security Enhancement ◽

Human Interactions

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

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A Detection algorithm based on Convolutional Neural Network

10.20944/preprints201811.0583.v1 ◽

2018 ◽

Author(s):

Fei Rong ◽

Li Shasha ◽

Xu Qingzheng ◽

Liu Kun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Training Model ◽

Test Sample ◽

Training Sample ◽

Detection Algorithm ◽

Small Scale ◽

Detection Model ◽

Sample Data ◽

Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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