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破損花菇機(jī)器視覺檢測技術(shù)
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國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系資助項(xiàng)目(2008BBC012)、中央高?;究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(2010JC006)和華中農(nóng)業(yè)大學(xué)優(yōu)博優(yōu)碩基金資助項(xiàng)目


Application of Machine Vision in Detection of Broken Shiitake
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    摘要:

    為實(shí)現(xiàn)基于機(jī)器視覺技術(shù)的破損花菇自動(dòng)檢測,研究了基于曲線演化和花菇邊緣灰度分析的破損檢測方法以及破損花菇在線檢測系統(tǒng)。去除花菇背景,跟蹤花菇邊緣,得到花菇邊緣坐標(biāo)曲線,對此曲線的內(nèi)外部進(jìn)行曲線演化,并計(jì)算內(nèi)外部演化曲線與原始花菇邊緣曲線接近的點(diǎn)的個(gè)數(shù)(Nin、Nout),以此參數(shù)可判定花菇的破損狀況;利用形態(tài)學(xué)腐蝕的方法對花菇邊緣進(jìn)行采樣,從采樣灰度序列中提取均值(μ)、方差(ρ)、平均波峰寬度(Lp)和最大波峰寬度(Lmax)4個(gè)破損特征參數(shù),進(jìn)而使用模式識(shí)別的方法分析此4個(gè)破損特征參數(shù),得出花菇的破損狀況。結(jié)合曲線演化和邊緣灰度分析的結(jié)果聯(lián)合判斷花菇的破損狀況。對180個(gè)花菇樣本進(jìn)行測試,得出最終破損識(shí)別率為88.33%,檢測速度為98個(gè)/min。

    Abstract:

    In order to detect the broken shiitake, a automatic detection system of shiitake was developed based on machine vision. Identification algorithms based on curve evolution and shiitake edge grayscale were presented. First, the background of shiitake images was removed, the edge of shiitake was tracked and then the coordinates of shiitake boundary was obtained. A closed curve was composed of these coordinates. Two initial curves could be generated from the interior and exterior of the closed curve respectively. Two final curves were evolved on those two initial curves, which met the condition of specific termination criterion. Two parameters (Nin,Nout) were extracted from the difference of final curves and initial curve. These parameters could determine the broken extent of shiitake and shiitake edge region were sampled with the method of morphology. Then four parameters could be extracted from the sequence of the gray scale of sampled regions .These parameters were mean (μ), variance (ρ), average width of peaks (Lp) and the maximum width of peaks (Lmax) respectively. The four parameters were analyzed with the method of pattern recognition and the broken extent of shiitake was obtained from the processing results. The final result was given with the results of both curve evolution and grayscale analysis. Experiments show that the accuracy of final shiitake detection model reached up to 88.33% , and the selection speed was 98 per minute.

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陳 紅,夏 青,左 婷,譚鶴群,邊銀丙.破損花菇機(jī)器視覺檢測技術(shù)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2014,45(11):60-67. Chen Hong, Xia Qing, Zuo Ting, Tan Hequn, Bian Yinbing. Application of Machine Vision in Detection of Broken Shiitake[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(11):60-67.

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  • 收稿日期:2013-12-31
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  • 在線發(fā)布日期: 2014-11-10
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