scholarly journals Hierarchical CNN-based real-time fatigue detection system by visual-based technologies using MSP model

2018 ◽  
Vol 12 (12) ◽  
pp. 2319-2329 ◽  
Author(s):  
Wang Huan Gu ◽  
Yu Zhu ◽  
Xu Dong Chen ◽  
Lin Fei He ◽  
Bing Bing Zheng
Keyword(s):  
Real Time ◽  
Detection System ◽  
Fatigue Detection

scholarly journals A novel real-time driving fatigue detection system based on wireless dry EEG

2018 ◽  
Vol 12 (4) ◽  
pp. 365-376 ◽  
Author(s):  
Hongtao Wang ◽  
Andrei Dragomir ◽  
Nida Itrat Abbasi ◽  
Junhua Li ◽  
Nitish V. Thakor ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driving Fatigue ◽  
Fatigue Detection

Implementation of Driver Fatigue Detection System with PCNN Algorithm Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 1060-1064 ◽  
Author(s):  
Yang Lu ◽  
Chao Gao
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Detection System ◽  
Good Stability ◽  
The Real ◽  
Design And Implementation ◽  
Car Accidents ◽  
Stability And Accuracy ◽  
Eye Closure ◽  
Fatigue Detection

This work presents the design and implementation of drivers fatigue detection system based on FPGA to prevent car accidents. According to the bright pupil phenomenon, which is produced by the retina when the incident lights wavelength is 850 nm, drivers eyes can be detected easily. While acquiring the real-time video of the drivers face by camera, the system accomplishes the detection of drivers eyes by using a simplified PCNN (pulse coupled neural network) and the computation of the PERCLOS (Percentage of Eye Closure) to decide whether the driver is fatigue or not. All the designing and accomplishments of the system are based on the FPGA platform Xilinx Virtex Pro Development Board. During the experiments, the system has the ability of processing 25 frames/sec, which is the speed of collection of the used camera. Also, the fatigue detection system has good stability and accuracy.

Real-Time Vision-Based Driver Drowsiness/Fatigue Detection System

2010 ◽  
Author(s):  
K. P. Yao ◽  
W. H. Lin ◽  
C. Y. Fang ◽  
J. M. Wang ◽  
S. L. Chang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driver Drowsiness ◽  
Fatigue Detection

scholarly journals Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12491-12498 ◽  
Author(s):  
Burcu Kir Savas ◽  
Yasar Becerikli
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driver Fatigue ◽  
Fatigue Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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