Authors:
Ekrem Misimi
1
;
John R. Mathiassen
2
;
Ulf Erikson
2
and
Amund Skavhaug
3
Affiliations:
1
SINTEF Fisheries and Aquaculture; Norwegian University of Science and Technology, Norway
;
2
SINTEF Fisheries and Aquaculture, Norway
;
3
Norwegian University of Science and Technology, Norway
Keyword(s):
Computer vision, feature extraction, fish grading, processing line, Atlantic salmon.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Intensive use of manual labour is necessary in the majority of operations in today’s fish processing plants, incurring high labour costs, and human mistakes in processing, evaluation and assessment. Automatization of processing line operations is therefore a necessity for faster, low-cost processing. In this paper, we present a computer vision system for sorting Atlantic salmon according to size and shape. Sorting is done into two grading classes of salmon: “Production Grade” and “Superior/Ordinary Grade”. Images of salmon were segmented into binary images, and then feature extraction was performed on the geometrical parameters to ensure separability between the two grading classes. The classification algorithm was a threshold type classifier. We show that our computer vision system can be used to evaluate and sort salmon by shape and deformities in a fast and non-destructive manner. Today, the low-cost of implementing advanced computer vision solutions makes this a real possibility
for replacing manual labour in fish processing plants.
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