结合稀疏表示和深度学习的伏牛山猕猴桃表面缺陷动态检测分类模型研究

项目名称： 结合稀疏表示和深度学习的伏牛山猕猴桃表面缺陷动态检测分类模型研究

项目编号： No.61503202

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 刘伟

作者单位： 南阳师范学院

项目金额： 21万元

中文摘要： 伏牛山猕猴桃作为南水北调中线水源地的特色经济作物，对带动水源地的农牧业发展具有重大的示范意义。现阶段由于缺少可靠系统的新理论模型，基于猕猴桃的表面缺陷分级技术无法有效的实施。猕猴桃粗糙的果皮给缺陷检测增加了难度，缺陷种类的多样性又增大了分类的复杂性，而近年来稀疏表示和深度学习理论分别在图像去噪和分类的部分领域取得了突破性进展。鉴于此，本项目拟开展结合稀疏表示和深度学习的伏牛山猕猴桃表面缺陷动态检测分类模型的研究，这对于系统化提高基于计算机视觉的农产品无损分级技术的理论层次具有重要的科学意义。本项目将以猕猴桃表面缺陷的去噪检测、分类及跟踪统计为主线构建完整的动态检测分类模型，主要研究内容包括：基于多核字典稀疏滤波和颜色空间正交变换的表面缺陷去噪检测模型的构建；结合深度学习和稀疏表示的表面缺陷分类理论和技术；多视角多特征表面缺陷跟踪与定量分析理论与方法。

中文关键词： 表面缺陷检测分类；采后自动化分级；动态模型；稀疏表示；深度学习

英文摘要： Funiu Mountian kiwi fruit is a main economic crop in the water source area of the middle route project of the south-to-north water transfer. The industrialization of this kind of fruit is of great significance demonstration to improve the local economic development. At this stage, due to the lack of a new reliable and systematic theoretical model, the postharvest automatic grading technology based on fruit surface defect detection and classification can not be effectively implemented. The rough surfaces of kiwi fruits increase the difficulty to defect detection, and the diversity of defect types increases the complexity of defect classification. In recent years, the theories of sparse representation and deep learning have made breakthrough progresses in the fields of image denoising and image classification respectively. Given this, the purpose of this project is to carry out research of Funiu Mountain kiwi fruit surface defect dynamic detection and classification model based on sparse representation and deep learning. It has important scientific significance to systematically improve the agricultural non-destructive grading theoretical level based on computer vision. The main research strand of this project is to construct a complete dynamic fruit surface defect detection and classification model, which includes the fruit image denoising, surface defect detection, surface defect classification and surface defect statistics. The main research topics include: Firstly, the construction of surface defect detection model based on multi-core dictionary sparse filter and color space orthogonal transformation will be developed. Secondly, we will focus on the surface defect classification theory and technology combined with deep learning and sparse representation. Finally, we will propose a multi-perspective and multi-feature surface defect tracking and quantitative analysis theory.

英文关键词： surface defect detection and classification ;postharvest automatic grading ;dynamic model;sparse representation ;deep learning