Food Detection with Image Processing Using Convolutional Neural Network (CNN) Method
Objective
Currently, the payment process at restaurants is still manual and inefficient because it uses a cash register. To reduce difficulty in this process, we introduce a novel food recognition and automatic bill generation using deep learning techniques.
Abstract
In this paper, food detection aims to facilitate payment at restaurants, and automatic food price estimation is by using the network. Because of the wide diversity of types of food, image recognition of food items is generally very difficult. We applied AlexNet to the tasks of food detection and recognition through image processing techniques. We constructed a dataset of the most frequent food items in Kaggle, and used it to evaluate recognition performance.
The network classifies the train data and test data and gives the results of classified output. However, deep learning has been shown recently to be a very powerful image recognition technique, and it is a state-of-the-art approach to deep learning. This network obtained 95% of accuracy more than existing methods.
Keywords: Image recognition, AlexNet, Image processing, Convolutional Neural Network (CNN).
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 Math Works 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 be 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 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.
