Paper Info
Title
Defect Detection and Classification in Citrus Using Computer Vision
Abstract
In this paper, a system for quality control in citrus is presented. In current citrus manufacturing industries, calliper and color are successfully used for the automatic classification of fruits using vision systems. However, fault detection in the citrus surface is carried out by means of human inspection. In this work, a computer vision system capable of detecting defects in the citrus peel and also classifying the type of flaw is presented. First, a review of citrus illnesses has been carried out in order to build a database of digitalized oranges classified by the kind of fault, which is used as a training set. The segmentation of faulty zones is performed by applying the Sobel gradient to the image. Afterwards, color and texture features of the flaw are extracted, some of them related with high order statistics. Several techniques have been employed for classification purposes: Euler distance to a prototype, to the nearest neighbor and k-nearest neighbors. Additionally, a three layer neural network has been tested and compared, obtaining promising results.
Year
DOI
Venue
2009
10.1007/978-3-642-10684-2_2
ICONIP
Keywords
Field
DocType
high order statistic,current citrus,citrus surface,citrus illness,computer vision system,vision system,citrus peel,classification purpose,computer vision,fault detection,defect detection,automatic classification,k nearest neighbor,nearest neighbor,manufacturing industry,neural network,quality control
k-nearest neighbors algorithm,Training set,Computer vision,Pattern recognition,Segmentation,Computer science,Fault detection and isolation,Sobel operator,Artificial intelligence,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
5864
0302-9743
3
PageRank 
References 
Authors
0.57
2
3
Name
Order
Citations
PageRank
Jose J. Lopez1324.51
Emanuel Aguilera291.38
Maximo Cobos316220.52