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基于多特征融合和水平集的碧根果品質(zhì)檢測
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中國博士后科學(xué)基金項(xiàng)目(2017M611737)和國家自然科學(xué)基金面上項(xiàng)目(61772242,、61572239)


Detection of Pecan Quality Based on Multi-feature Fusion and Level Set
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    摘要:

    碧根果在生產(chǎn)加工過程中易酸敗,,誤食會對人體造成多方面危害,。針對此問題,,提出一種基于多特征融合和水平集的碧根果品質(zhì)檢測方法。以薄殼碧根果為研究對象,,首先,,對采集的原始圖像進(jìn)行預(yù)處理,解決目標(biāo)對象與背景區(qū)域比例不匹配問題,;然后,,通過改進(jìn)邊緣指示函數(shù)的自適應(yīng)距離正則化水平集算法(Distance regularized level set evolution, DRLSE)對圖像進(jìn)行感興趣區(qū)域(Region of interest, ROI)分割,最后提取圖像灰度直方圖統(tǒng)計(jì)特征,、灰度共生矩陣,、Tamura和局部二值模式等多特征,并進(jìn)行融合分析,,建立支持向量機(jī)(Support vector machine, SVM)判別模型,,實(shí)現(xiàn)碧根果無損品質(zhì)檢測。試驗(yàn)采集了200個正常,、酸敗碧根果樣本圖像,,對其進(jìn)行圖像酸敗及多特征分析,。結(jié)果表明,采用本文方法判別碧根果酸敗的分類準(zhǔn)確率高達(dá)96.15%,,在此基礎(chǔ)上識別碧根果酸敗程度,,平均識別率為90.81%。

    Abstract:

    Pecan is one of the top ten nuts in the world. Because of its good taste and rich nutrition, it is loved by people. But pecan is easy to deteriorate in the process of production and processing. Mistaken food can cause many hazards to human body. To solve this problem, a method for detecting the quality of pecans was proposed based on multifeature fusion and level set. Taking thinshelled pecans as research object, and the original image was preprocessed to solve the problem that the target object did not match the background area. The adaptive DRLSE method with improved edge indication function was used to segment the pecans in the image, and the statistical features of the gray histogram of the image were extracted. Multifeatures such as cooccurrence matrix, Tamura and local binary mode were combined and analyzed. The SVM discriminant model was established to realize the nondestructive quality detection of pecans. The experiment collected 200 normal, rancid pecans sample images, and subjected to image rancidity and multifeature analysis. The experimental results showed that the adaptive DRLSE segmentation method with improved edge indication function can complete the segmentation better than the traditional method even inside or outside the target. The accuracy of the method was as high as 96.15% in judging whether pecan was rancid or not, and on this basis, the average recognition rate was 90.81% in judging the degree of pecan rancidity.

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劉哲,鄒小波,宋余慶,王明,蘇駿.基于多特征融合和水平集的碧根果品質(zhì)檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2019,50(12):348-356,364. LIU Zhe, ZOU Xiaobo, SONG Yuqing, WANG Ming, SU Jun. Detection of Pecan Quality Based on Multi-feature Fusion and Level Set[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(12):348-356,364.

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  • 收稿日期:2019-06-10
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  • 在線發(fā)布日期: 2019-12-10
  • 出版日期: 2019-12-10
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