Técnicas de Visión Artificial para Clasificación de Fresas (Artificial Vision Techniques to Optimize Strawberry's Industrial Classification)

Patricia Constante (pnconstante@espe.edu.ec)1, A. Gordon (amgordon@espe.edu.ec)1, Oscar Chang (amgordon@espe.edu.ec)1, E. Pruna (eppruna@espe.edu.ec)1, Fausto Acuña (fvacunia@espe.edu.ec)1, I. Escobar (ipescobar@espe.edu.ec)1


1Universidad de las Fuerzas Armadas - ESPE

This paper appears in: Revista IEEE América Latina

Publication Date: June 2016
Volume: 14,   Issue: 6 
ISSN: 1548-0992


Abstract:
This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learning. This combination originates a strong network - object relations which makes possible the recognition of complex strawberry features under changing conditions of lightning, size and orientation. The programming uses OpenCV libraries and fruits databases captured with a webcam. The images used to train the Artificial Neural Network are defined with canny edge detection and a moving region of interest (ROI). After training, the network recognizes important features such as shape, color and anomalies. The system has been tested in real time with real images.

Index Terms:
Artificial intelligence, Artificial neural networks, Image processing, Machine vision   


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