Mr. John Wannan Profile

John Wannan

at Falcon Foodservice Equipment

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 30 September 2024 Presentation + Paper

Food recognition and segmentation using embedded hardware

James Rainey, Inés Blach, Douglas MacLachlan, John Wannan, Deepayan Bhowmik

Proceedings Volume 13137, 131370H (2024) https://doi.org/10.1117/12.3031247

KEYWORDS: Image segmentation, Data modeling, Performance modeling, Object detection, Internet of things

Read Abstract +

Food recognition plays a vital role in various domains including dietary monitoring, nutrition analysis and food service automation. Real-world food recognition is a challenging task, as the contents of a plate of food can be complex intermixed objects, making it difficult to define their individual structures. Currently, technology offers a wide range of feasible options for dietary assessment and image-based methods hold the capability of substituting for traditional methods such as food records, food frequency questionnaires, and 24-hour recalls, which can have low accuracy and provide unreliable results. Faced with these issues, deep learning methods have shown better accuracy and ability to identify ingredients and types of food compared to traditional approaches for image classification. However, many deep learning methods rely on powerful computational resources which have limitations in terms of cost, energy consumption, and size. The rapid evolution of embedded hardware systems has significantly influenced the domain of computer vision and offers promising solutions to these challenges.

This paper presents a method that utilises deep-learning methods for detection and segmentation that are optimised for resource-constrained embedded platforms. These networks are tailored to efficiently process food images while ensuring low latency and energy efficiency. Additionally, strategies such as model quantisation, pruning, and compression are employed to reduce the computational complexity and memory footprint, making them suitable for deployment on embedded devices with limited resources, such as a Raspberry Pi. The method consists of a custom recognition pipeline that makes use of YOLOv8¹ and EdgeSAM² approaches for detection and segmentation, which are trained on the foodSeg103³ dataset. The resulting system provides a fast, accurate way to recognise foods without requiring expensive, energy intensive hardware.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years