scholarly journals Electronic Eye for Blind People with Object Detection and Audio Output Using Image Processing

2021 ◽  
pp. 403-406
Author(s):  
Atharva Shewale ◽  
Mrunalini Mahakalkar ◽  
Vijay Pawar ◽  
Yajan Bharad ◽  
Dr. Shwetambari Chiwhane
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Object Detection ◽  
Real Time ◽  
Blind People ◽  
Blind Person ◽  
Great Problem ◽  
Unknown Environment ◽  
Audio Output ◽  
Electronic Eye

One of the major issues faced by Blind people is detecting and recognizing an object. The objective of this project is to help the blind people because mobility of blind people is always a great problem. The mobility of blind people in unknown environment seems impossible without external help, because they don’t have any proper idea about their surroundings. So, we are developing a electronic eye which helps them to know about their surroundings and also guide them during travelling. Developing a system based on image processing using DNN algorithm which is able to labeling objects with the help of OpenCV and Tensor flow libraries and converting the labeled text in to speech and producing output in the form of audio to make the blind person aware of the object in front of him or her. The scope of this system is also measuring the distance of the object from the person and reporting the same Object detection using image processing and Machine Learning. It searches the object. We want to innovate our system the possibility of using the hearing sense to understand real time objects. For the security purpose track blind people in real time environment.

scholarly journals Real-Time Object Detection Using Yolo Algorithm for Blind People

2021 ◽  
pp. 152-156
Author(s):  
Prof. Pradnya Kasture ◽  
Akshay Tangade ◽  
Aditya Pole ◽  
Aishwarya Kumkar ◽  
Yash Jagtap
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Blind People ◽  
Human Beings ◽  
Blind Person ◽  
Detection Algorithms ◽  
Visually Challenged ◽  
Audio Output ◽  
Better Than ◽  
Detection And Recognition

Vision is one of the foremost necessary sense that human beings use to interact with the surrounding objects. There are more than 200 visually challenges people in this world and being visually challenged obstruct lots of daily activities. Hence it is very important for blind person to know what objects they are interacting with and understand their surroundings. In this project we have created a website, which help the blind people to identify different objects in the surrounding using YOLO V3 algorithm. This integrates different technologies to build a rich website which not only helps to recognize different object in the visually challenged persons surrounding in real time but also guides them through an audio output. YOLO (You Only Look at Once) algorithm is used for object detection and recognition. This algorithm gives very close accuracy for object detection in real time and studies have also proven the this algorithm is faster and better than other object detection algorithms.

scholarly journals Real Time Facial Emotion Recognition based on Image Processing and Machine Learning

2016 ◽  
Vol 139 (11) ◽  
pp. 16-19 ◽  
Author(s):  
Rituparna Halder ◽  
Sushmit Sengupta ◽  
Arnab Pal ◽  
Sudipta Ghosh ◽  
Debashish Kundu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Emotion Recognition ◽  
Real Time ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Implementation of Tensor Flow for Real-time Object Detection

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2342-2345
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Open Source ◽  
Real Time ◽  
Mobile Web ◽  
Ongoing Process

Tensor Flow is an open-source Machine Learning library for research and creation. Tensor Flow offers APIs for beginners and specialists to create for work desktop, mobile, web, and cloud. The best utilizations of Google's Tensor flow are the best applications for deep learning . Deep Learning is extraordinary at example acknowledgment/machine recognition, and it's being connected to pictures, video, sound, voice, content and time arrangement information. It groups and bunch information like that with now and again superhuman precision. This can be actualized for the acknowledgment of the diverse items, for example, Ball, Cat, Bottle, Car and so forth. It can utilize Android as its stage with to utilize the cell phone's camera to prepare the informational indexes and perceive diverse items in ongoing process.

scholarly journals Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment

2020 ◽  
Vol 6 (4) ◽  
pp. 1-4
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Real Time ◽  
Clustering Algorithm ◽  
Agricultural Land ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Crop Diseases ◽  
Internet Of Things Technology

India is an agricultural country. A total of 61.5% of the people cultivate in India. Due to lack of agricultural land and change of weather, manytypes of diseases occur on crops and insects are born.Therefore, the production of crops is coming down. To reduce this problem, Internet of Things technology will prove to be an important role. In this system, a sensor network will be created on agricultural land using Raspberry Pi 3 model. The images of the crops will be taken by sensor cameras and these images will be sent to the cloud server via Raspberry Pi 3 model. In this proposed methodology, various image processing techniques willbe apply on acquired images for classification of crop diseases using k-means clustering algorithm with unsupervised machine learning. This paper will also shows the method of image processing technique such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases.In bad natural environment, the farmers can produce quality crops and people will get healthy foodby this proposed methodologyand make more profit.In real time treatme

scholarly journals Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1932
Author(s):  
Malik Haris ◽  
Adam Glowacz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Single Shot ◽  
Automated Driving ◽  
Convolutional Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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