Paper PublishingiWaste: Video-Based Medical Waste Detection and Classification
Objective
To replace the current time-intensive and dangerous manual waste auditing method, we propose a system named iWASTE to detect and classify medical waste based on videos recorded by a camera-equipped waste container.
Abstract
In this work, we will detect and classify the medical waste from the input video which is nearly 5 seconds in duration. The collected video contains 4 number of medical waste things such as, gloves, hairnet, mask, shoe cover. Waste monitoring is necessary for the efficient reduction of medical waste in operation theatre.
We suggest a framework known as iWASTE (Intelligent Waste Auditing System for Tracking Emissions) to detect and classify medical waste based on video records by a camera placed on waste container to improve the previous time-consuming and unsafe manual waste assessment process. For detection and classification process, we propose a new architecture based on Deep Learning Techniques.
The proposed method will obtain a promising result when compared to pre-existing methods.
Keywords: Video Processing, Detection, Classification, iWaste, Deep Learning Techniques
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software & Hardware Requirements:
Software: Matlab 2018a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
- Introduction to Matlab
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- Matlab coding skills
- About tools & libraries
- Application Program Interface in Matlab
- About Matlab desktop
- How to use Matlab editor to create M-Files
- Features of Matlab
- Basics on Matlab
- What is an Image/pixel?
- About image formats
- Introduction to Image Processing
- How digital image is formed
- Importing the image via image acquisition tools
- Analyzing and manipulation of image.
- Phases of image processing:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will able to know, what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to detect and classify the object using AI
- How to extend our work to another real time applications
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
