【摘要】：枸杞的分級(jí)目前主要依靠人工進(jìn)行,這種分級(jí)方法受人的主觀意愿的影響,分級(jí)不準(zhǔn)確,同時(shí)費(fèi)時(shí)費(fèi)力,生產(chǎn)效率不高,不僅不能保證枸杞的外觀品質(zhì),而且無(wú)法滿(mǎn)足市場(chǎng)上品種多樣化的需求。因此,本文采用機(jī)器視覺(jué)技術(shù)對(duì)枸杞的表面品質(zhì)進(jìn)行了檢測(cè)識(shí)別及分級(jí),實(shí)現(xiàn)枸杞的自動(dòng)、無(wú)損檢測(cè)。針對(duì)枸杞的外觀品質(zhì),主要涉及枸杞的大小、表面缺陷、顏色等三個(gè)特征,主要研究?jī)?nèi)容和結(jié)果如下:(1)針對(duì)枸杞分級(jí)檢測(cè)中對(duì)圖像特征提取的需要,對(duì)幾種常用顏色空間模型的特點(diǎn)分別進(jìn)行了系統(tǒng)的分析和闡述,同時(shí)對(duì)圖像的灰度化、二值化、圖像的分割、圖像的邊緣檢測(cè)、圖像濾波等圖像的基礎(chǔ)算法進(jìn)行了分析和研究,選擇出適于枸杞外觀品質(zhì)檢測(cè)分級(jí)的預(yù)處理算法。(2)為了實(shí)現(xiàn)對(duì)枸杞大小的分級(jí),通過(guò)研究獲取枸杞的投影表面積、長(zhǎng)軸直徑、短軸直徑、偏心率、圓度等幾何特征參數(shù)的方法和技術(shù),建立了枸杞長(zhǎng)軸徑的人工測(cè)量值與像素值之間的相關(guān)模型。(3)根據(jù)色度值識(shí)別出破損和油粒區(qū)域,再通過(guò)濾波處理,統(tǒng)計(jì)破損和油粒區(qū)域的面積像素個(gè)數(shù),根據(jù)這部分區(qū)域與整個(gè)健全枸杞面積的比值進(jìn)行對(duì)缺陷程度的判別,實(shí)現(xiàn)對(duì)枸杞表面缺陷程度的檢測(cè)與判斷,正確率達(dá)93.4%。(4)以HSI顏色空間模型為依據(jù),提取枸杞的顏色特征參數(shù)。用色度的范圍對(duì)枸杞進(jìn)行分級(jí),最后計(jì)算色度、飽和度、亮度的均值、方差,判斷分級(jí)的準(zhǔn)確性。(5)利用Matlab/GUI軟件平臺(tái)開(kāi)發(fā)一套枸杞自動(dòng)檢測(cè)評(píng)價(jià)系統(tǒng),該軟件采用模塊化設(shè)計(jì),包括文件模塊、圖像采集模塊、圖像預(yù)處理模塊,特征參數(shù)提取模塊和分級(jí)模塊。實(shí)現(xiàn)了人機(jī)合一的可視化操作,界面友好,操作簡(jiǎn)單,易于維護(hù)。試驗(yàn)結(jié)果證明,利用該系統(tǒng)對(duì)枸杞進(jìn)行檢測(cè)分級(jí)速度快,準(zhǔn)確率高。
[Abstract]:At present, the classification of Lycium barbarum is mainly carried out manually. This classification method is influenced by people's subjective will. It is inaccurate, laborious and inefficient in production. It is not only unable to guarantee the appearance quality of Lycium barbarum. And unable to meet the market variety of demand. Therefore, the surface quality of Lycium barbarum was detected and classified by machine vision technology, and the automatic and nondestructive testing of Lycium barbarum was realized. Aiming at the appearance quality of Lycium barbarum, it mainly involves the size, surface defect and color of Lycium barbarum. The main research contents and results are as follows: (1) aiming at the need of image feature extraction in classification detection of Lycium barbarum, The characteristics of several commonly used color space models are systematically analyzed and expounded. At the same time, the basic algorithms of image grayscale, binarization, image segmentation, image edge detection, image filtering and so on are analyzed and studied. In order to realize the classification of the size of Lycium barbarum, the projection surface area, long axis diameter, short axis diameter, eccentricity rate of Lycium barbarum were obtained by studying the surface area, long axis diameter, short axis diameter and eccentricity of Lycium barbarum. Based on the method and technique of geometric characteristic parameters such as roundness, the correlation model between the measured value and pixel value of the long axis diameter of Lycium barbarum is established. (3) according to the chroma value, the damaged and oil particle regions are identified, and then processed by filtering. According to the ratio of this area to the whole intact wolfberry area, the defect degree is judged, and the surface defect degree of Lycium barbarum is detected and judged. (4) based on HSI color space model, the color characteristic parameters of Lycium barbarum were extracted. This paper classifies Lycium barbarum with the range of chrominance, calculates the mean value, variance of chrominance, saturation and brightness, and judges the accuracy of classification. (5) A set of automatic detection and evaluation system of Lycium barbarum is developed by using Matlab/GUI software platform. The software is designed by modularization, including file module, image acquisition module, image preprocessing module, feature parameter extraction module and classification module. The visual operation is realized, the interface is friendly, the operation is simple and easy to maintain. The experimental results show that the system is fast and accurate in the detection and classification of Lycium barbarum.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S567.19;TP391.41

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