A Deep Learning Framework Using Convolutional Neural Network for Multi-Class Object Recognition

Hayat, Shaukat; She, Kun; Tengtao, Zuo; Yu, Yue; Tu, Tianyi; Du, Yantong

doi:10.1109/icivc.2018.8492777

Cited by 38 publications

(16 citation statements)

References 15 publications

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“…Computer vision is a field where understanding of human vision can be duplicated. Often referred to as image analysis [16]. The idea is to use computer vision for object detection, that could probably help the blind to visualize the surrounding environment better, using Image-to-Text and Text-to-Voice, without any complex hardware [18].…”

Section: Design Implementationmentioning

confidence: 99%

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

et al. 2020

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Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

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Section: Design Implementationmentioning

confidence: 99%

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

et al. 2020

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“…Finally, the Fully connected layers classify the data. CNN has been implemented in various applications such as: object recognition [14], [15], handwriting classification [16], [17] and image classification [3], [18], [19], to name a few.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

Castillo

Granados

Ariza

2019

Cien.Ing.Neogranadina

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Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.

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“…The first is the introduction of a new image representation called the Integral Image which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on Ada Boost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers [6]. The third contribution is a method for combining increasingly more complex classifiers in a cascade which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions.…”

Section: Rapid Object Detection Using a Boosted Cascade Of Simple Featuresmentioning

confidence: 99%

Survey of Object Detection using Deep Neural Networks

Swetha¹,

Muneshwara²,

Thungamani³

2018

International Journal of Advanced Research in Computer and Comm

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Object detection using deep neural network especially convolution neural networks. Object detection was previously done using only conventional deep convolution neural network whereas using regional based convolution network [3] increases the accuracy and also decreases the time required to complete the program. The dataset used is PASCAL VOC 2012 which contains 20 labels. The dataset is very popular in image recognition, object detection and other image processing problems. Supervised learning is also possible in implementing the problem using Decision trees or more likely SVM. But neural network work best in image processing because they can handle images well.

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A Deep Learning Framework Using Convolutional Neural Network for Multi-Class Object Recognition

Cited by 38 publications

References 15 publications

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

Survey of Object Detection using Deep Neural Networks

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