scholarly journals Gender recognition from unconstrained selfie images: a convolutional neural network approach

2022 ◽  
Vol 12 (2) ◽  
pp. 2066
Author(s):  
Saddam Bekhet ◽  
Abdullah M. Alghamdi ◽  
Islam F. Taj-Eddin
Keyword(s):  
Deep Learning ◽  
Real Life ◽  
Surveillance Systems ◽  
Gender Recognition ◽  
Learning Approaches ◽  
Body Images ◽  
Neural Network Approach ◽  
Benchmark Datasets ◽  
Targeted Marketing ◽  
Small Change

<p>Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and<br />CAS-PEAL-R1.</p>

scholarly journals Deep Learning Approaches for Object Detection

2020 ◽  
pp. 123-145
Author(s):  
Sushma Jaiswal ◽  
Tarun Jaiswal
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
General Idea ◽  
Detection Methods ◽  
Learning Approaches ◽  
Detection Techniques ◽  
Second Stage ◽  
Fast Pace ◽  
Benchmark Datasets

In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.

scholarly journals Multiple Object Tracking in Deep Learning Approaches: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2406
Author(s):  
Yesul Park ◽  
L. Minh Dang ◽  
Sujin Lee ◽  
Dongil Han ◽  
Hyeonjoon Moon
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Motion Trajectory ◽  
Learning Approaches ◽  
Multiple Object ◽  
Problems And Solutions ◽  
Benchmark Datasets ◽  
Future Work ◽  
Appearance Changes

Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

scholarly journals Challenges of Deep Learning-based Text Detection in the Wild

2021 ◽  
Vol 6 (1) ◽  
pp. 1-5
Author(s):  
Zobeir Raisi ◽  
Mohamed A. Naiel ◽  
Paul Fieguth ◽  
Steven Wardell ◽  
John Zelek
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Text Detection ◽  
Detection Methods ◽  
Learning Approaches ◽  
Plane Rotation ◽  
The Past ◽  
Perspective Distortion ◽  
Benchmark Datasets ◽  
In The Wild

The reported accuracy of recent state-of-the-art text detection methods, mostly deep learning approaches, is in the order of 80% to 90% on standard benchmark datasets. These methods have relaxed some of the restrictions of structured text and environment (i.e., "in the wild") which are usually required for classical OCR to properly function. Even with this relaxation, there are still circumstances where these state-of-the-art methods fail.  Several remaining challenges in wild images, like in-plane-rotation, illumination reflection, partial occlusion, complex font styles, and perspective distortion, cause exciting methods to perform poorly. In order to evaluate current approaches in a formal way, we standardize the datasets and metrics for comparison which had made comparison between these methods difficult in the past. We use three benchmark datasets for our evaluations: ICDAR13, ICDAR15, and COCO-Text V2.0. The objective of the paper is to quantify the current shortcomings and to identify the challenges for future text detection research.

REVIEW ON DEEP LEARNING APPROACHES TO THE CONTROL OF PROSTHETIC HANDS WITH ELECTROMYOGRAPHY SIGNALS

2021 ◽  
pp. 249-252
Author(s):  
Shahana Parveen ◽  
Nisheena V Iqbal
Keyword(s):  
Deep Learning ◽  
Real Life ◽  
Learning Approaches ◽  
Control Methods ◽  
Natural Control ◽  
Prosthetic Hands ◽  
Control Robustness ◽  
Dexterous Hand ◽  
Prosthesis Control ◽  
Electromyography Signals

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. However, the control robustness offered by scientic research is still not sufcient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. This paper reviews various papers on deep learning approaches to the control of prosthetic hands with EMG signals and made a comparison on their accuracy.

Benchmarking Deep Learning for On-Board Space Applications

2021 ◽  
Vol 13 (19) ◽  
pp. 3981
Author(s):  
Maciej Ziaja ◽  
Piotr Bosowski ◽  
Michal Myller ◽  
Grzegorz Gajoch ◽  
Michal Gumiela ◽  
...  
Keyword(s):  
Deep Learning ◽  
Experimental Validation ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Real Life ◽  
Space Applications ◽  
Deep Model ◽  
Learning Techniques ◽  
Standard Tool ◽  
Benchmark Datasets

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain.

Object Recognition Using Deep Learning

2019 ◽  
Vol 16 (9) ◽  
pp. 4044-4052 ◽  
Author(s):  
Rohini Goel ◽  
Avinash Sharma ◽  
Rajiv Kapoor
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Object Recognition ◽  
Deep Neural Network ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Learning Frameworks

The deep learning approaches have drawn much focus of the researchers in the area of object recognition because of their implicit strength of conquering the shortcomings of classical approaches dependent on hand crafted features. In the last few years, the deep learning techniques have been made many developments in object recognition. This paper indicates some recent and efficient deep learning frameworks for object recognition. The up to date study on recently developed a deep neural network based object recognition methods is presented. The various benchmark datasets that are used for performance evaluation are also discussed. The applications of the object recognition approach for specific types of objects (like faces, buildings, plants etc.) are also highlighted. We conclude up with the merits and demerits of existing methods and future scope in this area.

scholarly journals HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

2021 ◽  
Vol 13 (8) ◽  
pp. 1440
Author(s):  
Yi Zhang ◽  
Lei Fu ◽  
Ying Li ◽  
Yanning Zhang
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Aerial Images ◽  
Aerial Image ◽  
Sufficient Information ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Deep Learning Features ◽  
Global And Local

Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.

scholarly journals Deep Learning to Handle Congestion in Vehicle Routing Problem: A Review

2021 ◽  
Vol 2129 (1) ◽  
pp. 012023
Author(s):  
D Satyananda ◽  
A Abdullah
Keyword(s):  
Deep Learning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Traffic Data ◽  
Real Situation ◽  
Traffic Condition ◽  
Routing Problem ◽  
The Real ◽  
Benchmark Datasets

Abstract This paper reviews the implementation design of Deep Learning in Vehicle Routing Problem. Congestion and traffic condition are usually avoided in Vehicle Routing Problem due to its modeling complexity, and even the benchmark datasets only cover essential conditions. In the real situation, the traffic condition is varied, and congestion is the worst part. To model the real life, the delivery route must consider these situations. The vehicle needs information on traffic prediction in future time to avoid congestion. The prediction needs historical traffic data, which is very large. Deep Learning can handle the enormous size and extract data features to infer the prediction.

scholarly journals Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7073
Author(s):  
Ivan Kuric ◽  
Jaromír Klarák ◽  
Milan Sága ◽  
Miroslav Císar ◽  
Adrián Hajdučík ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Polynomial Regression ◽  
Real Life ◽  
Inspection System ◽  
Laser Sensor ◽  
Learning Approaches ◽  
Real Representation ◽  
Tire Industry ◽  
Inspection Systems

At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. In this paper, we describe the development of a tire inspection system for the tire industry. We provide methods for processing tire sidewall data obtained from a camera and a laser sensor. The captured data comprise visual and geometric data characterizing the tire surface, providing a real representation of the captured tire sidewall. We use an unfolding process, that is, a polar transform, to further process the camera-obtained data. The principles and automation of the designed polar transform, based on polynomial regression (i.e., supervised learning), are presented. Based on the data from the laser sensor, the detection of abnormalities is performed using an unsupervised clustering method, followed by the classification of defects using the VGG-16 neural network. The inspection system aims to detect trained and untrained abnormalities, namely defects, as opposed to using only supervised learning methods.

Abstract 238: A Deep Neural Network Approach for Continuous Electrocardiogram-based Shock Advisory System During Cardio Pulmonary Resuscitation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Shirin Hajeb Mohammadalipour ◽  
Alicia Cascella ◽  
Matt Valentine ◽  
K.H. Chon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Motion Artifacts ◽  
Learning Approaches ◽  
Neural Network Approach ◽  
Advisory System ◽  
Deep Learning Algorithm ◽  
Cardio Pulmonary Resuscitation

The ability of an automatic external defibrillator (AED) to make a reliable shock decision during cardio pulmonary resuscitation (CPR) would improve the survival rate of patients with out-of-hospital cardiac arrest. Since chest compressions induce motion artifacts in the electrocardiogram (ECG), current AEDs instruct the user to stop CPR while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. While deep learning approaches have been used successfully for arrhythmia classification, their performance has not been evaluated for creating an AED shock advisory system that can coexist with CPR. To this end, the objective of this study was to apply a deep-learning algorithm using convolutional layers and residual networks to classify shockable versus non-shockable rhythms in the presence and absence of CPR artifact using only the ECG data. The feasibility of the deep learning method was validated using 8-sec segments of ECG with and without CPR. Two separate databases were used: 1) 40 subjects’ data without CPR from Physionet with 1131 shockable and 2741 non-shockable classified recordings, and 2) CPR artifacts that were acquired from a commercial AED during asystole delivered by 43 different resuscitators. For each 8-second ECG segment, randomly chosen CPR data from 43 different types were added to it so that 5 non-shockable and 10 shockable CPR-contaminated ECG segments were created. We used 30 subjects’ and the remaining 10 for training and test datasets, respectively, for the database 1). For the database 2), we used 33 and 10 subjects’ data for training and testing, respectively. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for both datasets using the four-fold cross-validation were found to be 95.21% and 86.03%, respectively. For shockable versus non-shockable classification of ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. These results meet the AHA sensitivity requirement (>90%).

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