Open AccessA flour impurity detection system based on image processing
Author(s) -
Jimin Zhao,
Chenchen Xue,
Chenchen Xue
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012129
Subject(s) - grayscale , artificial intelligence , computer vision , edge detection , image processing , image segmentation , impurity , computer science , segmentation , entropy (arrow of time) , binary image , pattern recognition (psychology) , image (mathematics) , chemistry , physics , organic chemistry , quantum mechanics
Flour plays an important role in People’s Daily consumption, and the content of impurities in flour indicates the quality of flour. At present, most domestic factories are using magnifying glass and other simple tools for impurity detection. This method is troublesome and does not meet the requirement of precision. This paper designs an automatic impurity detection system based on image processing, which not only improves the detection efficiency, but also greatly improves the detection accuracy. The basic process of this system is to grayscale the image obtained by photographing, then carry out local entropy transformation, and then map to form entropy image. Finally, the impurity detection is completed after image filtering, image segmentation and edge detection.