Mechanical bruise detection of nectarine based on polarization imaging technology

WANG Liang; YANG Yu; HUANG Min; ZHU Qibing

doi:10.7510/jgjs.issn.1001-3806.2022.06.021

LASER TECHNOLOGY > 2022 > 46(6): 841-849. > DOI: 10.7510/jgjs.issn.1001-3806.2022.06.021

WANG Liang, YANG Yu, HUANG Min, ZHU Qibing. Mechanical bruise detection of nectarine based on polarization imaging technology[J]. LASER TECHNOLOGY, 2022, 46(6): 841-849. DOI: 10.7510/jgjs.issn.1001-3806.2022.06.021

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Mechanical bruise detection of nectarine based on polarization imaging technology

Key Laboratory of Light Industry Process Advanced Control, Ministry of Education, School of Internet of things engineering, Jiangnan University, Wuxi 214122, China

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Received Date: October 27, 2021
Revised Date: December 02, 2021
Published Date: November 24, 2022

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Abstract

Abstract

To solve the problem that the mechanical bruise of nectarine is difficult to be effectively detected due to the complex color features of nectarine skin, a polarization imaging technology was introduced into the mechanical bruise detection of nectarines. A pixel-level bruise classification model based on polarization imaging technology was proposed. In the experiment, the division of focal plane (DoFP) polarization camera was utilized to capture the degree of polarization images in the four polarization directions respectively. Firstly, bilinear interpolation was utilized to reduce the dimension of the polarization image cube to improve the operation speed of the whole algorithm, and low-light image enhancement (LIME) was utilized to compensate for the shape of nectarine fruit and to improve the light intensity of nectarine edge area, so as to reduce the influence of fruit curvature change. Secondly, the color features and gray-level co-occurrence matrix (GLCM) features of positive (bruised) and negative (non-bruised) pixels in the preprocessed image were extracted. Then, two least squares support vector machine (LSSVM) classifiers were trained independently based on the two features. Finally, two classifiers (color-LSSVM model and GLCM-LSSVM model) were connected in series to realize bruise detection. Results show that: Two independent classifiers with radial basis function (RBF) as kernel function were used in series (color-LSSVM→GLCM-LSSVM model) with the precision of 95.68% and the recall of 93.29%. This study proves that DoFP polarization imaging technology has a prosperous application prospect in the field of non-destructive detection of mechanical bruises of dark fruits.
- imaging systems,
- polarization imaging,
- mechanical bruise,
- non-destructive detection,
- nectarines,
- least squares support vector machine

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References

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