scholarly journals REAL TIME MOVABLE OBJECT DETECTION, TRACKING AND VELOCITY ESTIMATION USING IMAGE PROCESSING

2015 ◽  
Vol 3 (01) ◽  
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Real Time ◽  
Velocity Estimation ◽  
Movable Object

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.

Role of Clustering Techniques in Effective Image Segmentation

2018 ◽  
pp. 128-160
Author(s):  
Bhavneet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Object Detection ◽  
Real Time ◽  
Vision Research ◽  
Medical Equipment ◽  
Additional Advantage ◽  
Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

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 A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing

2014 ◽  
Vol 91 (16) ◽  
pp. 38-42 ◽  
Author(s):  
C. Hemalatha ◽  
S. Muruganand ◽  
R. Maheswaran
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Real Time ◽  
Tracking And Recognition

Model Klasifikasi Mata Katarak dan Normal Menggunakan Histogram

2017 ◽  
Vol 9 (1) ◽  
pp. 33-36
Author(s):  
Valencia Wirawan ◽  
Yustinus Eko Soelistio
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Index Terms

Telah banyak penelitian pada citra medis telah diadopsi oleh sebagian besar ilmuwan dan dokter yang dapat membantu dalam mendeteksi gangguan pada mata terutama katarak. Namun, umumnya penelitian tersebut menggunakan citra medis atau digital yang relatif mahal dan sulit didapatkan oleh sebagian orang, dan metode yang rentan akan translasi (pergeseran), serta perubahan ukuran gambar dan bentuk objek. Penelitian ini mengembangkan sebuah metode menggunakan model histogram untuk mengklasifikasi mata katarak dari citra digital dengan (1) format yang lebih umum seperti JPEG dan (2) lebih toleranterhadap translasi dan perubahan ukuran. Metode ini juga mampu bekerja dengan baik menggunakan citra digital dalam citra mata yang tidak tegak lurus terhadap kamera. Metode ini mencapai akurasi 79,03% dalam kondisi bebas dan 88.47% dalam kondisi mata tegak lurus terhadap kamera. Metode ini mempunyai kompleksitas yang rendah sehingga dapat digunakan pada komputer dengan spesifikasi rendah dan sistem yang membutuhkan kecepatan mendekati real-time. Index Terms—Image processing, cataract, classification, histogram

Development of real time welding control system by using image processing.

1989 ◽  
Vol 7 (3) ◽  
pp. 363-367 ◽  
Author(s):  
Takaichi Koyama ◽  
Yoichi Takahashi ◽  
Masahiro Kobayashi ◽  
Junichiro Morisawa
Keyword(s):  
Image Processing ◽  
Control System ◽  
Real Time
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