A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework

Quick and accurate crash detection is important for saving lives and improved traffic incident management. In this paper, a feature fusion-based deep learning framework was developed for video-based urban traffic crash detection task, aiming at achieving a balance between detection speed and accuracy with limited computing resource. In this framework, a residual neural network (ResNet) combined with attention modules was proposed to extract crash-related appearance features from urban traffic videos (i.e., a crash appearance feature extractor), which were further fed to a spatiotemporal feature fusion model, Conv-LSTM (Convolutional Long Short-Term Memory), to simultaneously capture appearance (static) and motion (dynamic) crash features. The proposed model was trained by a set of video clips covering 330 crash and 342 noncrash events. In general, the proposed model achieved an accuracy of 87.78% on the testing dataset and an acceptable detection speed (FPS > 30 with GTX 1060). Thanks to the attention module, the proposed model can capture the localized appearance features (e.g., vehicle damage and pedestrian fallen-off) of crashes better than conventional convolutional neural networks. The Conv-LSTM module outperformed conventional LSTM in terms of capturing motion features of crashes, such as the roadway congestion and pedestrians gathering after crashes. Compared to traditional motion-based crash detection model, the proposed model achieved higher detection accuracy. Moreover, it could detect crashes much faster than other feature fusion-based models (e.g., C3D). The results show that the proposed model is a promising video-based urban traffic crash detection algorithm that could be used in practice in the future.

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Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.687288 ◽

2021 ◽

Vol 15 ◽

Author(s):

Ming Yang ◽

Menglin Cao ◽

Yuhao Chen ◽

Yanni Chen ◽

Geng Fan ◽

...

Keyword(s):

Deep Learning ◽

Functional Connectivity ◽

Large Scale ◽

Network Connectivity ◽

Autism Spectrum ◽

Detection Accuracy ◽

Learning Framework ◽

Proposed Model ◽

Deep Learning Model

GoalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.MethodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.ResultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.ConclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD.SignificanceThese findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.

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A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network

Electronics ◽

10.3390/electronics8080825 ◽

2019 ◽

Vol 8 (8) ◽

pp. 825 ◽

Cited By ~ 8

Author(s):

Rui Huang ◽

Jinan Gu ◽

Xiaohong Sun ◽

Yongtao Hou ◽

Saad Uddin

Keyword(s):

Deep Learning ◽

Intelligent Manufacturing ◽

Detection Methods ◽

Detection Accuracy ◽

Electronic Components ◽

Recognition Method ◽

Image Labeling ◽

Fast Recognition ◽

Speed And Accuracy ◽

Detection Speed

Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. In this paper, a fast recognition method for electronic components in a complex background is presented. Firstly, we built the image dataset, including image acquisition, image augmentation, and image labeling. Secondly, a fast recognition method based on deep learning was proposed. The balance between detection accuracy and detection speed was solved through the lightweight improvement of YOLO (You Only Look Once)-V3 network model. Finally, the experiment was completed, and the proposed method was compared with several popular detection methods. The results showed that the accuracy reached 95.21% and the speed was 0.0794 s, which proved the superiority of this method for electronic component detection.

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Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327908666180727123657 ◽

2019 ◽

Vol 9 (1) ◽

pp. 16-31

Author(s):

Kyungkoo Jun

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Short Term Memory ◽

Window Size ◽

Sensor Data ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

10.2196/preprints.19407 ◽

2020 ◽

Author(s):

Jinseok Lee

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Ct Images ◽

Chest Ct ◽

University Hospital ◽

Detection Accuracy ◽

Ct Image ◽

Test Dataset ◽

Learning Framework ◽

Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-2326 ◽

2021 ◽

pp. 182-189

Author(s):

S. Arokiaraj ◽

Dr. N. Viswanathan

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Short Term Memory ◽

The Other ◽

Learning Models ◽

Computational Overhead ◽

Temporal Features ◽

Human Movements ◽

Proposed Model ◽

Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

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Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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Deep Learning Assisted Covid-19 Detection using full CT-scans

10.36227/techrxiv.13162049.v1 ◽

2020 ◽

Author(s):

varan singhrohila ◽

Nitin Gupta ◽

Amit Kaul ◽

Deepak Sharma

Keyword(s):

Deep Learning ◽

Ct Scans ◽

Detection Accuracy ◽

Residual Network ◽

Excellent Performance ◽

Examination Procedure ◽

Medical Institutions ◽

Proposed Model ◽

Learning Technique ◽

Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

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Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

Computational Intelligence and Neuroscience ◽

10.1155/2017/4216281 ◽

2017 ◽

Vol 2017 ◽

pp. 1-22 ◽

Cited By ~ 34

Author(s):

Jihyun Kim ◽

Thi-Thu-Huong Le ◽

Howon Kim

Keyword(s):

Deep Learning ◽

Power Consumption ◽

Distribution System ◽

Short Term Memory ◽

Electrical Power ◽

Electricity Consumption ◽

Classification Performance ◽

Robust Solution ◽

Proposed Model ◽

Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

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EEG and Deep Learning Based Brain Cognitive Function Classification

Computers ◽

10.3390/computers9040104 ◽

2020 ◽

Vol 9 (4) ◽

pp. 104

Author(s):

Saraswati Sridhar ◽

Vidya Manian

Keyword(s):

Deep Learning ◽

Motor Imagery ◽

Cognitive Aging ◽

Classification Accuracy ◽

Short Term Memory ◽

Principal Component ◽

Learning System ◽

Detection Accuracy ◽

Band Power ◽

Signal Features

Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.

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Application of Color Featuring and Deep Learning in Maize Plant Detection

Remote Sensing ◽

10.3390/rs12142229 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2229

Author(s):

Haojie Liu ◽

Hong Sun ◽

Minzan Li ◽

Michihisa Iida

Keyword(s):

Deep Learning ◽

False Negative ◽

Color Space ◽

Performance Comparison ◽

Maize Plant ◽

Detection Accuracy ◽

Learning Technology ◽

Practical Applications ◽

Feature Based ◽

Detection Speed

Maize plant detection was conducted in this study with the goals of target fertilization and reduction of fertilization waste in weed spots and gaps between maize plants. The methods used included two types of color featuring and deep learning (DL). The four color indices used were excess green (ExG), excess red (ExR), ExG minus ExR, and the hue value from the HSV (hue, saturation, and value) color space, while the DL methods used were YOLOv3 and YOLOv3_tiny. For practical application, this study focused on performance comparison in detection accuracy, robustness to complex field conditions, and detection speed. Detection accuracy was evaluated by the resulting images, which were divided into three categories: true positive, false positive, and false negative. The robustness evaluation was performed by comparing the average intersection over union of each detection method across different sub–datasets—namely original subset, blur processing subset, increased brightness subset, and reduced brightness subset. The detection speed was evaluated by the indicator of frames per second. Results demonstrated that the DL methods outperformed the color index–based methods in detection accuracy and robustness to complex conditions, while they were inferior to color feature–based methods in detection speed. This research shows the application potential of deep learning technology in maize plant detection. Future efforts are needed to improve the detection speed for practical applications.

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