Grading system of pear's appearance quality based on computer vision

IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology Pub Date : 2012-05-19 DOI:10.1109/ICSAI.2012.6223414

Zhongzhi Han, Jing Liu, Yougang Zhao, Yanzhao Li

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引用次数: 2

Abstract

In order to realize the automatic grading system of pear's appearance quality based on computer vision, a grading system is constructed which includes hardware and software environment. It includes transport unit, control module, image acquisition module, and image processing and recognition module. It can realize automatic feature extraction. The features include shape, color and defect. According to the influence of spots on pear's surface on the defect detection, a spot removal method based on V component's dynamic threshold is put forward. According to the national standards, grading rules of fruit type and defect levels based on above features are constructed. Furthermore grading model of pear based on artificial neural network is also constructed. The recognition rate of 630 images of pear reaches up to 90.3%. The system, equipment and method in this paper have positive significance to on-line grading of pear's quality of appearance.

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基于计算机视觉的梨外观质量分级系统

为了实现基于计算机视觉的梨外观质量自动分级系统，构建了一个包括硬件环境和软件环境的分级系统。它包括传输单元、控制模块、图像采集模块和图像处理与识别模块。可以实现特征的自动提取。特征包括形状、颜色和缺陷。针对梨表面斑点对缺陷检测的影响，提出了一种基于V分量动态阈值的斑点去除方法。根据国家标准，构建了基于上述特征的水果类型分级规则和缺陷等级。在此基础上，建立了基于人工神经网络的梨树分级模型。630张梨图像的识别率达到90.3%。该系统、设备和方法对梨外观质量在线分级具有积极意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology

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