本文選題：蛋形曲線 + RANSAC擬合曲線�。� 參考：《廣東工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著我國養(yǎng)殖業(yè)的迅速發(fā)展,雞蛋產(chǎn)量和銷量占據(jù)國際市場的大量份額,雞蛋品質(zhì)的檢測顯得十分重要。雞蛋表面有很多的微生物和細菌,在雞蛋運輸過程中,會因磕碰、擠壓等外在因素而造成外殼破損,微生物和細菌很容易污染雞蛋內(nèi)的營養(yǎng)物質(zhì),還會感染周圍完好的雞蛋,不僅給廠家?guī)砭薮蟮慕?jīng)濟損失,還給消費者帶來健康隱患。蛋類加工業(yè)對雞蛋的缺陷檢測研究刻不容緩�；谝曈X技術(shù)的檢測技術(shù)因為具有高精度、高效率、無損壞等優(yōu)點在蛋類加工業(yè)中受到越來越多的關(guān)注。形狀缺陷檢測常用基于曲線擬合的方法,通常用于擬合直線、圓、橢圓等,鮮有對雞蛋外形的擬合研究,原因在于蛋形曲線的準(zhǔn)確描述難度較高。常見的基于Hough變換和基于最小二乘法的曲線擬合受噪聲影響較大,擬合精度和運行效率低。傳統(tǒng)的裂紋缺陷檢測常用基于紋理特征的算法和基于形態(tài)特征的算法,兩者都存在如何最大程度保存裂紋特征的問題。因此,研究雞蛋缺陷檢測具有重要的現(xiàn)實意義。本文搭建基于視覺的雞蛋缺陷檢測系統(tǒng),比較各種光源的參數(shù)確定激光照明器,比較相機性能確定CCD相機采集圖片。通過確定蛋形曲線方程以及擬合蛋形曲線條件,采用單模和多模RANSAC算法對雞蛋形狀進行擬合,檢測雞蛋是否存在外形缺陷。通過對預(yù)處理后的裂紋圖像進行高頻增強濾波,然后紋理特征的局部最大差值法和形態(tài)學(xué)處理相結(jié)合,提取裂紋特征,檢測雞蛋是否存在裂紋缺陷。根據(jù)實驗結(jié)果可知,本文提出的雞蛋外形缺陷檢測算法具有魯棒性,準(zhǔn)確率為95.4%,召回率為96.1%;本文提出的雞蛋裂紋缺陷檢測算法的準(zhǔn)確率為96.8%,召回率為97.5%。
[Abstract]:With the rapid development of China's aquaculture industry, egg production and sales occupy a large share of the international market, the detection of egg quality is very important. There are a lot of microbes and bacteria on the surface of eggs. During the transportation of eggs, the shell will be damaged by bumping, squeezing and other external factors. Microorganisms and bacteria will easily contaminate the nutrients in the eggs, and they will also infect the eggs in good condition around them. Not only bring huge economic losses to manufacturers, but also bring health risks to consumers. It is urgent to study the defect detection of eggs in egg processing industry. Because of its advantages of high precision, high efficiency and no damage, visual detection technology has attracted more and more attention in egg processing industry. Shape defect detection is usually based on curve fitting, which is usually used to fit straight line, circle, ellipse and so on. There is little research on egg shape fitting, because it is difficult to accurately describe egg shape curve. The common curve fitting based on Hough transform and least square method is greatly affected by noise, and the fitting accuracy and running efficiency are low. The traditional methods of crack defect detection are based on texture feature and shape feature. Both of them have the problem of how to preserve the crack feature to the maximum extent. Therefore, the study of egg defect detection has important practical significance. In this paper, an egg defect detection system based on vision is set up. The parameters of various light sources are compared to determine the laser illuminator, and the performance of the camera is compared to determine the CCD camera to collect pictures. By determining the egg shape curve equation and fitting the egg shape curve condition, single mode and multi mode RANSAC algorithm were used to fit the egg shape to detect whether the egg had any shape defect. After preprocessing the crack image is filtered by high frequency enhancement, then the local maximum difference method of texture feature is combined with morphological processing to extract the crack feature and detect whether the egg has crack defect or not. According to the experimental results, the algorithm proposed in this paper is robust, the accuracy is 95.4and the recall rate is 96.1.The accuracy of the algorithm is 96.8and the recall rate is 97.5.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S879.3;TP391.41

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