Object Detection Using Machine Learning for Visually Impaired People

Mandhala, Venkata Naresh; Bhattacharyya, Debnath; Vamsi, Bandi; Rao, N. Thirupathi

doi:10.31782/ijcrr.2020.122032

Cited by 23 publications

(6 citation statements)

References 11 publications

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“…Considering two classes, the standard CM frequently has four factors as follows: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). In this work, to analyze the performance evaluation for the proposed model, the accuracy metric is utilized as in (1), which is considered as a result of the experiments.…”

Section: Methodsmentioning

confidence: 99%

“…The abilities of visually impaired people for detection and recognition of banknotes are limited or influenced. Owing to this incentive, many visually impaired people bring a sighted friend or family member to assist them in their daily financial activities [1]. Due to repeated use, tactile markings on the banknote's surface disappear or fade away, making it a frustrating task for blind people to recognize and count various types of banknotes by touch.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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“…Takizawa et al [14] presented an object recognition model with the help of computer vision technique like edge prediction that can guide the visually-impaired users to identify the type of object. Mandhala et al [15] proposed machine learning-based solution named clustering technique to classify the multi-class object. Bhole et al [16], used deep learning techniques such as Single Shot Detector (SSD) and Inception V3 to classify bank concurrency notes in real-time environment.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Audio Assistive System for Visually Impaired People

Masud¹,

Jeribi²,

Alhameed³

et al. 2022

Computers, Materials &Amp; Continua

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Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the current research work presents an Efficient Object Detection Model with Audio Assistive System (EODM-AAS) using DL-based YOLO v3 model for visually-impaired people. The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people. The presented model involves a DL-based YOLO v3 model for multi-label object detection. Besides, the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people. In order to validate the detection performance of the presented method, a detailed simulation analysis was conducted on four datasets. The simulation results established that the presented model produces effectual outcome over existing methods.

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“…As the model convolves itself into a lone neural network, jumps straight to the image pixels to bounding box coordinates and object classes. Mandhala, V.N., Bhattacharyya, D., Vamsi, B., Thirupathi Rao, N. [8] in 2018 have developed an Object Detection model to Assist Visually Impaired People. Their contribution focused on developing computer vision algorithms combined with a deep neural network to assist visually impaired individual's mobility in clinical environments by accurately detecting doors, stairs, and signages, the most remarkable landmarks.…”

Section: Cognitive Model For Object Detection Based On Speech-to-text...mentioning

confidence: 99%

Cognitive Model for Object Detection based on Speech-to-Text Conversion

Jithendra

Sai

Mannam

et al. 2020

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)

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The goal of this paper is to develop a model which is the integrated version of both SpeechRecognition and Object detection. This model is developed after undergoing the literature survey and the existing models that are related to Object Detection and Speech Recognition. There are several types of Speech Recognition and Object Detection models available so far. In addition to the existing models, this paper proposes a new model named "Cognitive Model for Object Detection based on Speech-to-Text Conversion," which is an integrated version of both Speech Recognition and Object Detection models. Firstly, A speech command is provided as an input to the model, it takes the command and processes the data, and then it detects the specified object from a source of images. The detected object is represented with a rectangular box. This approach is implemented with the help of Google Speech Recognition and YOLO object detection models utilizing the Darknet and OpenCV frameworks.

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Object Detection Using Machine Learning for Visually Impaired People

Cited by 23 publications

References 11 publications

Deep learning-based iraqi banknotes classification system for blind people

Deep learning-based iraqi banknotes classification system for blind people

Deep Learning Based Audio Assistive System for Visually Impaired People

Cognitive Model for Object Detection based on Speech-to-Text Conversion

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