AI-based machine vision for retail self-checkout system
(2019) In Master's Theses in Mathematical Sciences FMAM05 20191Mathematics (Faculty of Engineering)
- Abstract
- In recent years advances in computing power, availability of large
annotated datasets and AI algorithms have enabled the rise of reliable
object identification and tracking. This thesis describes the development
and implementation considerations of a system for object detection in
the retail industry. This project have been conducted as a collaboration
between ETH Zürich and start-up company AI Retailing Systems, who
wants to automate parts of the retailing experience, namely the checkout
procedure in a retail store. A data set of images with corresponding
bounding boxes and pixel-segmentations has been gathered, consisting
of ten Swiss retail products. Relevant theory is discussed and three state
of the art neural network... (More) - In recent years advances in computing power, availability of large
annotated datasets and AI algorithms have enabled the rise of reliable
object identification and tracking. This thesis describes the development
and implementation considerations of a system for object detection in
the retail industry. This project have been conducted as a collaboration
between ETH Zürich and start-up company AI Retailing Systems, who
wants to automate parts of the retailing experience, namely the checkout
procedure in a retail store. A data set of images with corresponding
bounding boxes and pixel-segmentations has been gathered, consisting
of ten Swiss retail products. Relevant theory is discussed and three state
of the art neural network architectures are reviewed and evaluated for the
specific application and dataset. The thesis concludes with a discussion
of the main challenges for this type of solution, a recommendation for
the object detection model to be used and pointers for future work. (Less) - Popular Abstract (Swedish)
- Vi har alla stått alldeles för länge i en kö för att handla den där chipspåsen, lunchlådan eller lördagsgodiset. Att handla mat är ett måste för oss alla, och de flesta av oss föredrar att få det överstökat så snabbt som möjligt. Detaljhandeln erbjuder snabbkassor, men nu är det dags för nästa steg, helt automatiska lösningar som använder den senaste tekniken inom datorseende och artificiell intelligens.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8985308
- author
- Rigner, Anton LU
- supervisor
- organization
- alternative title
- AI-baserat datorseende för automatiserad betalningsprocedur i detaljhandeln
- course
- FMAM05 20191
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Object detection, Retail, Automatic Checkout, Artificial Intelligence, Deep Learning, Computer Vision
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3387-2019
- ISSN
- 1404-6342
- other publication id
- 2019:E33
- language
- English
- id
- 8985308
- date added to LUP
- 2019-07-16 13:38:03
- date last changed
- 2019-12-30 03:39:07
@misc{8985308, abstract = {{In recent years advances in computing power, availability of large annotated datasets and AI algorithms have enabled the rise of reliable object identification and tracking. This thesis describes the development and implementation considerations of a system for object detection in the retail industry. This project have been conducted as a collaboration between ETH Zürich and start-up company AI Retailing Systems, who wants to automate parts of the retailing experience, namely the checkout procedure in a retail store. A data set of images with corresponding bounding boxes and pixel-segmentations has been gathered, consisting of ten Swiss retail products. Relevant theory is discussed and three state of the art neural network architectures are reviewed and evaluated for the specific application and dataset. The thesis concludes with a discussion of the main challenges for this type of solution, a recommendation for the object detection model to be used and pointers for future work.}}, author = {{Rigner, Anton}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{AI-based machine vision for retail self-checkout system}}, year = {{2019}}, }