本文選題：機(jī)器視覺 + 臍橙��； 參考：《浙江大學(xué)》2017年博士論文

【摘要】：缺陷檢測(cè)是水果分級(jí)的重要環(huán)節(jié)之一。由于水果表面缺陷呈多樣性和復(fù)雜性等特點(diǎn),水果表面缺陷的快速檢測(cè)一直是學(xué)術(shù)界和工業(yè)界的研究熱點(diǎn)。近年來,計(jì)算機(jī)視覺技術(shù)逐漸被應(yīng)用于農(nóng)產(chǎn)品的外部質(zhì)量檢測(cè)。本文以臍橙為對(duì)象,利用機(jī)器視覺技術(shù)詳細(xì)地研究探討臍橙表面8種類型常見缺陷(薊馬果、潰瘍果、裂傷果、日灼果、藥傷果、風(fēng)傷果、蟲傷果、介殼蟲果)檢測(cè)方法并提出相應(yīng)的算法,同時(shí)開發(fā)了臍橙缺陷的機(jī)器視覺自動(dòng)化檢測(cè)試驗(yàn)系統(tǒng),所提出的檢測(cè)方法對(duì)研發(fā)快速臍橙缺陷分級(jí)裝備奠定了基礎(chǔ)。論文的主要研究?jī)?nèi)容及成果如下:1)搭建適合水果表面缺陷檢測(cè)的視覺試驗(yàn)系統(tǒng)平臺(tái),包括基于傳統(tǒng)PC計(jì)算機(jī)視覺為基礎(chǔ)的可見光RGB成像系統(tǒng)、基于嵌入式系統(tǒng)的嵌入式機(jī)器視覺系統(tǒng)。2)針對(duì)類球形水果表面亮度分布不均勻干擾檢測(cè)現(xiàn)象,提出了一種新穎的臍橙表面缺陷快速多閾值邊緣分割算法。該方法能成功檢測(cè)出表面缺陷區(qū)域。此分割算法對(duì)薊馬果、潰瘍果、裂傷果、日灼果、藥傷果、風(fēng)傷果、蟲傷果、介殼蟲果等8類型缺陷臍橙的5008個(gè)感興趣區(qū)域進(jìn)行分割,獲得了92%分割精度。3)提出了一種新穎的臍橙表面灰度局部閾值快速分割算法。該方法能克服類球形水果表面亮度分布不均勻問題,該算法將積分圖理論和局部閾值計(jì)算相結(jié)合并成功分割臍橙表面缺陷區(qū)域,此分割算法對(duì)薊馬果、潰瘍果、裂傷果、日灼果、藥傷果、風(fēng)傷果、蟲傷果、介殼蟲果等類型缺陷臍橙進(jìn)行檢測(cè),獲得了95.2%檢測(cè)正確率,每一幅離線圖像處理時(shí)間是38.5ms。4)提出了一種新穎的臍橙表面亮度分布不均的快速自適應(yīng)亮度矯正及單閾值快速水果缺陷分割算法。該方法能使正常水果組織表面區(qū)域被矯正為高灰度區(qū)域,而水果表面的缺陷區(qū)域仍然保持低灰度區(qū)域,矯正克服了類球形水果表面亮度分布不均導(dǎo)致缺陷檢測(cè)誤檢的問題,這也為單閾值臍橙表面缺陷快速檢測(cè)提供了可能性。依據(jù)不同光照成像環(huán)境下試驗(yàn)表明,該算法可以直接對(duì)臍橙表面整體亮度進(jìn)行自適應(yīng)矯正,并且該自適應(yīng)亮度變換算法比現(xiàn)有文獻(xiàn)水果亮度變化算法相比速度快10倍以上。5)提出一種基于低成本小型多核嵌入式機(jī)器視覺的在線臍橙缺陷檢測(cè)自動(dòng)化系統(tǒng),并研究了其軟件和硬件系統(tǒng)的實(shí)現(xiàn),包括嵌入式在線圖像采集實(shí)現(xiàn),光照成像環(huán)境實(shí)現(xiàn),Linux嵌入式系統(tǒng)千兆網(wǎng)工業(yè)相機(jī)等相關(guān)硬件底層驅(qū)動(dòng)設(shè)計(jì),在線機(jī)器視覺的圖像算法設(shè)計(jì),Linux嵌入式系統(tǒng)在線檢測(cè)軟件架構(gòu)與設(shè)計(jì)等。試驗(yàn)結(jié)果表明該系統(tǒng)可以在單通道每秒7個(gè)臍橙的速度下,檢測(cè)正確率達(dá)到95.8%。
[Abstract]:Defect detection is one of the most important steps in fruit grading. Because of the diversity and complexity of fruit surface defects, rapid detection of fruit surface defects has been a hot spot in academia and industry. In recent years, computer vision technology has been gradually applied to the external quality detection of agricultural products. In this paper, eight common defects on navel orange surface (thrips, ulcer fruit, laceration fruit, sunburn fruit, medicinal fruit, wind fruit, insect fruit) were studied in detail by using machine vision technology. A machine vision automated testing system for navel orange defects was developed, which laid a foundation for the development of fast navel orange defect grading equipment. The main contents and results of this paper are as follows: (1) A visual test system platform for fruit surface defect detection is built, including a visible light RGB imaging system based on traditional PC computer vision. An embedded machine vision system based on embedded system. 2) A novel fast multi-threshold edge segmentation algorithm for surface defects of navel orange is proposed to detect the uneven luminance distribution on the surface of spherical fruit. This method can detect the surface defect area successfully. The segmentation algorithm was used to segment 5008 regions of interest in eight types of defective navel oranges, such as thrips, ulcerated fruit, lacerated fruit, sunburn fruit, medicinal fruit, wind fruit, insect fruit, mesoderma fruit, etc. The accuracy of 92% segmentation is obtained. 3) A novel fast segmentation algorithm for navel orange surface grayscale local threshold is proposed. This method can overcome the problem of uneven luminance distribution on the surface of globular fruit. The algorithm combines the integral graph theory with the local threshold calculation and successfully divides the surface defect area of navel orange. The algorithm is used to segment thrips, ulcers and cracked fruit. The accuracy of detecting navel orange with sunburn fruit, drug wound fruit, wind wound fruit, insect fruit, mesoderm fruit, etc., was obtained by 95.2%. The processing time of each offline image is 38.5 ms.4) A novel fast adaptive luminance correction algorithm with uneven luminance distribution on the surface of navel orange and a fast fruit defect segmentation algorithm with single threshold are proposed. This method can make the normal fruit tissue surface area be corrected as a high gray area, while the defect area of the fruit surface remains a low gray level area, which overcomes the problem that the uneven luminance distribution on the surface of globular fruit results in the false detection of defects. This also provides the possibility for rapid detection of single threshold navel orange surface defects. Experiments under different illumination imaging conditions show that the proposed algorithm can be used for adaptive correction of the overall brightness of navel orange surface directly. In addition, the adaptive luminance transform algorithm is more than 10 times faster than the existing fruit luminance change algorithm. (5) an online navel orange defect detection automation system based on low-cost multi-core embedded machine vision is proposed. The realization of software and hardware system is studied, including the realization of embedded online image acquisition, the realization of Linux embedded system gigabit network industrial camera, and the realization of illumination imaging environment. Image algorithm Design of online Machine Vision and Linux embedded system On-line Detection Software Architecture and Design. The experimental results show that the system can detect correctly at the speed of 7 navel oranges per second in a single channel.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TS255.7;TP391.41

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