Hutton, Jamie and Harper, Glyn and Duckett, Tom (2012) A prototype low-cost machine vision system for automatic identification and quantification of potato defects. In: The Dundee Conference - Crop Protection in Northern Britain 2012, 28-29 February 2012, Dundee, Scotland, UK.
Full content URL: http://www.sipr.ac.uk/CPNB/Index_and_Proceedings_2...
Hutton et al- CPNB 2012.pdf - Whole Document
|Item Type:||Conference or Workshop contribution (Other)|
|Item Status:||Live Archive|
This paper reports on a current project to develop a prototype system
for the automatic identification and quantification of potato defects based on
machine vision. The system developed uses off-the-shelf hardware, including a
low-cost vision sensor and a standard desktop computer with a graphics processing
unit (GPU), together with software algorithms to enable detection, identification
and quantification of common defects affecting potatoes at near-real-time frame
rates. The system uses state-of-the-art image processing and machine learning
techniques to automatically learn the appearance of different defect types. It also
incorporates an intuitive graphical user interface (GUI) to enable easy set-up of the
system by quality control (QC) staff working in the industry.
|Keywords:||machine learning, computer vision, food technology, semantic segmentation|
|Subjects:||G Mathematical and Computer Sciences > G760 Machine Learning|
G Mathematical and Computer Sciences > G740 Computer Vision
|Divisions:||College of Science > School of Computer Science|
|Deposited On:||16 Jul 2014 14:27|
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