scholarly journals Early warning system for drivers’ phone usage with deep learning network

2021 ◽  
Author(s):  
Jixu Hou ◽  
Xiaofeng Xie ◽  
Qian Cai ◽  
Zhengjie Deng ◽  
Houqun Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Warning System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Intelligent Monitoring ◽  
Learning Network ◽  
Dangerous Driving ◽  
Deep Learning Network ◽  
Deep Learning Model

Abstract Dangerous driving, e.g., using mobile phone while driving, can result in serious traffic problem and threat to safely. To efficiently alleviate such problem, in this paper, we design a intelligent monitoring system to detect the dangerous behavior in driving. The monitoring system is combined by camera, terminal server, target detection algorithm and voice reminder. Furthermore, we applied an efficiently deep learning model, namely mobilenet combined with single shot multi-box detector (mobilenet-SSD), to identify the behavior of driver. To evaluate the performance of proposed system, we construct a dangerous driving dataset which consists of 6796 images. The experimental results show that the proposed system can achieve accuracy of 99% in 100 testing images. It can be used for real-time monitoring of the driver’s status.

scholarly journals Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3030 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
P. Shakeel ◽  
H. Fouad ◽  
Yunyoung Nam ◽  
S. Baskar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Health Condition ◽  
Physical Activities ◽  
Edge Computing ◽  
Monitoring Systems ◽  
Multimedia Technology ◽  
Learning Network ◽  
Deep Learning Network ◽  
Multi Access

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

scholarly journals Diffeomorphic unsupervised deep learning model for mono- and multi-modality registration

2020 ◽  
Vol 14 ◽  
pp. 174830262097352
Author(s):  
Anis Theljani ◽  
Ke Chen
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Variational Model ◽  
Learning Network ◽  
Deep Learning Algorithm ◽  
Unsupervised Deep Learning ◽  
Deep Learning Network ◽  
Affine Transforms ◽  
Deep Learning Model

Different from image segmentation, developing a deep learning network for image registration is less straightforward because training data cannot be prepared or supervised by humans unless they are trivial (e.g. pre-designed affine transforms). One approach for an unsupervised deep leaning model is to self-train the deformation fields by a network based on a loss function with an image similarity metric and a regularisation term, just with traditional variational methods. Such a function consists in a smoothing constraint on the derivatives and a constraint on the determinant of the transformation in order to obtain a spatially smooth and plausible solution. Although any variational model may be used to work with a deep learning algorithm, the challenge lies in achieving robustness. The proposed algorithm is first trained based on a new and robust variational model and tested on synthetic and real mono-modal images. The results show how it deals with large deformation registration problems and leads to a real time solution with no folding. It is then generalised to multi-modal images. Experiments and comparisons with learning and non-learning models demonstrate that this approach can deliver good performances and simultaneously generate an accurate diffeomorphic transformation.

scholarly journals HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingfeng Jiang ◽  
Jiayan Gu ◽  
Yang Li ◽  
Bo Wei ◽  
Jucheng Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Short Term Memory ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Learning Network ◽  
Deep Convolution Neural Network ◽  
Deep Learning Network ◽  
Deep Learning Model ◽  
Fusion Features

In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long–short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

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