scholarly journals Hand Detection for Safety to Avoid Accidents

2021 ◽  
Vol 9 (VI) ◽  
pp. 3547-3552
Author(s):  
Eega Krishna Chaithanya
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Time Data ◽  
Image Processing Algorithm ◽  
Hand Detection ◽  
Safety Level ◽  
Real Time Data ◽  
Imaginary Line ◽  
Stop Line

Detecting the hand when it crosses the safety level and in return it also raises an alert in the form of alarm. So that the threat can be identified and proper measures are taken to overcome that. The methodology of the project goes as follows, taking input from camera , Image processing to detect hand, Projecting a line using computer vision, Raising alarm when hand crosses this projected safety line. The real time data is taken from the camera as an input to the Image processing algorithm. Then this input is processed to find the hand in image in it and checks whether the hand is crossing that safety line. If that hand is crossing the safety line we can simply raise alarm. The applications of the project are to the Employees who are working at industry are pushing the material into shredder machine. But somehow while pushing these material into shredder machine the employees are pushing their hands itself in the flow of work and the hands of employees were cut in that cause. So from a certain distance from shredder machine input we project a imaginary line using computer vision, So that if any hand crossing that imaginary line which is for safety we will raise an alarm. In addition, we can also extend the applications, by just replacing hand with the Bike, we can detect the bike, which is crossing the staggered stop line, and we can punish or fine them. As a part of object detection we are using Single short multibox detector.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

scholarly journals Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Froese ◽  
Joshua Dian ◽  
Carleen Batson ◽  
Alwyn Gomez ◽  
Amanjyot Singh Sainbhi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Integration ◽  
Real Time ◽  
Clinical Data ◽  
Biomedical Research ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Time Data ◽  
Optical Character ◽  
Real Time Data

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

Steps Toward Automated Deprocessing of Integrated Circuits

2017 ◽  
Author(s):  
E.L. Principe ◽  
Navid Asadizanjani ◽  
Domenic Forte ◽  
Mark Tehranipoor ◽  
Robert Chivas ◽  
...  
Keyword(s):  
Image Processing ◽  
Integrated Circuits ◽  
3D Imaging ◽  
Time Data ◽  
X Ray ◽  
Ongoing Work ◽  
Real Time Data ◽  
Instrument Control ◽  
Sem Imaging ◽  
Control Image

Abstract This paper discusses the development of an extensible programmatic workflow that leverages evolving technologies in 2D/3D imaging, distributed instrument control, image processing, and automated mechanical/chemical deprocessing technology. Initial studies involve automated backside mechanical ultra-thinning of 65nm node IC processor chips in combination with SEM imaging and X-ray tomography. Areas as large as 800μm x 800μm were deprocessed using gas-assisted plasma FIB delayering. Ongoing work involves enhancing the workflow with “intelligent automation” by bridging FIB-SEM instrument control and near real-time data analysis to establish a computationally guided microscopy suite.

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 Scratch Detection in Cars Using Mask Region Convolution Neural Networks

2020 ◽  
Author(s):  
Vijay K ◽  
Vijayakumar R ◽  
Sivaranjani P ◽  
Logeshwari R
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Quality Control ◽  
Automobile Industry ◽  
Digital Imaging ◽  
High Dimensional ◽  
Image Processing Algorithm ◽  
Aesthetic Standard ◽  
Fully Connected

This task depends on quality control in the vehicle business. It centers on the imprint and harms in new cars before producing to the client. This project presents the development of a system of recognition of defects and cosmetic imperfections in cars. This application gives a quick and strong robotized results. It likewise gives framework acknowledgement of scratches. In the as of now existing framework, the way toward distinguishing the scratches in car is finished by our mankind. Using the input frames, sections of the vehicles are entered for training, the last Fully-connected layer is altered so that it only has two exit categories: Sections with scratches and without scratches. This project is mainly developed to minimize manpower and maximize automation on quality department in automobile industry. It is a computer vision project. It includes task such as acquiring, processing, analyzing and understanding digital images and extraction of high dimensional data. An image processing algorithm is used in order to manipulate an image to achieve an aesthetic standard and to provide a translation between the human visual system and digital imaging services.

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