鮮杏表面缺陷的高光譜成像檢測研究
發(fā)布時(shí)間：2018-10-24 06:56
【摘要】：杏是新疆特色水果之一,其營養(yǎng)成分高又可作醫(yī)藥用途,備受消費(fèi)者青睞。鮮杏分級(jí)是產(chǎn)后處理的核心環(huán)節(jié),其中,鮮杏表面缺陷是等級(jí)劃分的重要評(píng)定指標(biāo)之一。目前國內(nèi)鮮杏和杏制品加工環(huán)節(jié)缺少成熟的自動(dòng)化檢測分級(jí)技術(shù),主要依靠人工進(jìn)行鮮杏缺陷的分選,效率低,勞動(dòng)強(qiáng)度大且品質(zhì)難以掌控。由于高光譜成像技術(shù)將圖像和光譜融合為一的特點(diǎn),因此,本文利用該技術(shù)對鮮杏的表面缺陷進(jìn)行快速的無損檢測探究。本文選取新疆鮮杏作為研究對象,基于可見/近紅外高光譜成像技術(shù)從圖像和光譜兩個(gè)角度對正常鮮杏、磨傷鮮杏、霉變鮮杏和蟲傷鮮杏進(jìn)行檢測研究,探求一種快速有效識(shí)別正常鮮杏和缺陷鮮杏的無損檢測方法,為搭建適于鮮杏缺陷的多光譜在線檢測系統(tǒng)奠定理論基礎(chǔ)。本文主要方法及研究結(jié)果如下:(1)確定了采集鮮杏圖像的具體參數(shù),采集正常、磨傷、霉變和蟲傷四種不同類型鮮杏的高光譜圖像,對鮮杏圖像的感興趣區(qū)域(ROI)進(jìn)行手動(dòng)提取,得到了各個(gè)波段下所對應(yīng)的光譜數(shù)據(jù)。(2)對不同類型鮮杏的原始光譜數(shù)據(jù),進(jìn)行S-G卷積平滑、標(biāo)準(zhǔn)正態(tài)變量(SNV)和多元散射校正(MSC)3種不同的預(yù)處理。對比了三種預(yù)處理方法對于SVM建模的影響。結(jié)果表明,原始光譜和S-G卷積平滑預(yù)處理建立的C-SVC類型的支持向量機(jī)模型較優(yōu),識(shí)別率均為93.3%。(3)對全波段鮮杏光譜數(shù)據(jù)采用PCA降維,通過權(quán)重系數(shù)優(yōu)選出495nm、570nm、729nm和891nm四個(gè)特征波段。分別對比了全波段和特征波段采用支持向量機(jī)、偏最小二乘判別、BP神經(jīng)網(wǎng)絡(luò)三種不同的判別方法的分類識(shí)別效果。結(jié)果表明:基于特征波段構(gòu)建的SVM預(yù)測模型,其識(shí)別結(jié)果整體上優(yōu)于全波段構(gòu)建的SVM預(yù)測模型,其中,對優(yōu)選的特征波段采用線性核函數(shù)構(gòu)建的C-SVC類型SVM的識(shí)別率為100%;全波段構(gòu)建的PLS-DA的檢測判別結(jié)果優(yōu)于采用特征波段構(gòu)建的檢測判別結(jié)果;利用全波段構(gòu)建的BP神經(jīng)網(wǎng)絡(luò)識(shí)別效果優(yōu)于特征波段構(gòu)建的BP神經(jīng)網(wǎng)絡(luò)識(shí)別效果。另外,利用特征波段構(gòu)建的SVM的識(shí)別效果優(yōu)于BP神經(jīng)網(wǎng)絡(luò)和PLS-DA。(4)對四種不同類型鮮杏高光譜圖像進(jìn)行全波段主成分分析和特征波段主成分分析,選擇缺陷部位明顯的PC圖像檢測判別。結(jié)果表明:利用全波段主成分分析,正常鮮杏和霉變鮮杏的分類準(zhǔn)確率較好,均達(dá)到100%,蟲傷鮮杏的判別率為88.3%,磨傷鮮杏識(shí)別率最低,僅為38.3%;采用特征波段主成分分析進(jìn)行檢測識(shí)別,正常鮮杏、霉變鮮杏和蟲傷鮮杏識(shí)別率分別為100%、100%、95%,磨傷鮮杏的判別準(zhǔn)確率提高到80%;全波段主成分分析的整體識(shí)別率為81.7%,而特征波段主成分分析整體識(shí)別率提高到93.8%。(5)對四種不同類型鮮杏高光譜圖像基于全波段和特征波段進(jìn)行最小噪聲分離變換,選擇缺陷部位明顯的MNF圖像進(jìn)行缺陷識(shí)別。結(jié)果表明:基于全波段的MNF,四種類型鮮杏的識(shí)別效果均較低,正常鮮杏和磨傷鮮杏的識(shí)別率分別為38.3%和33.3%,霉變鮮杏和蟲傷鮮杏識(shí)別率分別為53.3%、50%;基于特征波段的MNF,正常鮮杏和蟲傷鮮杏識(shí)別率提高到73.3%和71.7%;全波段最小噪聲分離處理的整體識(shí)別率為43.8%,而特征波段最小噪聲分離處理整體識(shí)別率為60%。通過比較,主成分分析的整體識(shí)別效果優(yōu)于最小噪聲分離處理的整體識(shí)別效果;對優(yōu)選的特征波段進(jìn)行PCA和MNF,其整體識(shí)別率分別提高到93.8%和60%;由此說明,特征波段的PCA可較有效地識(shí)別缺陷鮮杏和正常鮮杏。(6)為進(jìn)一步提高鮮杏缺陷的檢測率,嘗試對二次主成分分析檢測后未識(shí)別的磨傷鮮杏進(jìn)行圖像的波段比運(yùn)算。對4個(gè)特征波段下對應(yīng)的圖像進(jìn)行兩兩組合,選擇570nm/891nm波段比圖像進(jìn)行檢測。結(jié)果表明:磨傷鮮杏識(shí)別率由80%提高到88.3%。
[Abstract]:Apricot is one of Xinjiang's characteristic fruits, its nutrient content is high and can be used for medical use, which is favored by consumers. The classification of fresh apricot is the key link of post-treatment, among which the surface defect of fresh apricot is one of the important indexes of classification. At present, the processing links of fresh apricot and apricot products lack mature automatic detection and classification technology, and mainly rely on the sorting of fresh apricot defect artificially, the efficiency is low, the labor intensity is large, and the quality is difficult to control. Because hyperspectral imaging technology combines the image and the spectrum into one feature, this paper makes use of this technique to detect the surface defects of fresh apricot. In this paper, Xinjiang fresh apricot is selected as the research object, and based on the visible/ near-infrared hyperspectral imaging technology, the normal fresh apricot, the fresh apricot, the mildewed fresh apricot and the insect-wound fresh apricot are detected by the visible/ near-infrared hyperspectral imaging technology, A nondestructive testing method for quickly and effectively identifying normal fresh apricot and defective fresh apricot is studied, which lays a theoretical foundation for constructing multi-spectrum on-line detection system suitable for fresh apricot defect. The main methods and research results are as follows: (1) the specific parameters of the fresh apricot image are determined, the high-spectrum images of four different types of fresh apricot are collected, the region of interest (ROI) of the fresh apricot image is manually extracted, the corresponding spectral data in each band is obtained. (2) The original spectral data of different kinds of fresh apricot were processed by S-G convolution smoothing, standard positive-state variable (SNV) and multi-scatter correction (MSC). The effects of three pretreatment methods on SVM modeling were compared. The results show that the support vector machine model of C-SVC type established by the original spectrum and S-G convolution smoothing pre-processing is better, and the recognition rate is 93. 3%. (3) Using PCA to reduce the spectral data of the whole band, the four characteristic bands of 495nm, 570nm, 729nm and 891nm are determined by weight coefficient. The classification and recognition effects of three different discrimination methods of support vector machine, partial least two multiplication discrimination and BP neural network are compared respectively in the full band and the characteristic band. The results show that SVM prediction model based on feature band construction is superior to SVM prediction model constructed by full band, and the recognition rate of C-SVC type SVM constructed by linear kernel function for the preferred feature band is 100%. The detection result of PLS-DA constructed by the whole band is superior to the detection discrimination result constructed by the characteristic wave band, and the BP neural network identification effect constructed by the whole band is superior to the BP neural network identification effect constructed by the characteristic band. In addition, the recognition effect of SVM based on feature band is better than that of BP neural network and PLS-DA. (4) carrying out full band main component analysis and characteristic band main component analysis on four different types of fresh apricot high-spectrum images, and selecting obvious PC image detection discrimination on the defect parts. The results showed that the classification accuracy of fresh apricot and moldy fresh apricot reached 100% with the analysis of the main components of the whole band. The discrimination rate of fresh apricot was 88. 3%, the recognition rate of fresh apricot was 38. 3%, and the main component analysis of the characteristic bands was used to detect and identify the fresh apricot. The recognition rate of fresh apricot and worm was 100%, 100%, 95% respectively, and the accuracy of discrimination of fresh apricot was improved to 80%. The overall recognition rate of the whole band principal component analysis was 81.7%, while the overall recognition rate of the characteristic band principal component analysis was improved to 93.8%. and (5) carrying out minimum noise separation conversion on the four different types of fresh apricot high-spectrum images on the basis of the whole band and the characteristic wave band, and selecting the MNF images with obvious defect parts to perform defect identification. The results showed that the recognition rate of fresh apricot and fresh apricot was 35.3% and 33.3% respectively, and the recognition rate of fresh apricot and fresh apricot was 53.3% and 50%, respectively. The recognition rate of fresh apricot and fresh apricot was improved to 73. 3% and 71.7%, the overall recognition rate of the whole band minimum noise separation processing was 43.8%, and the overall recognition rate of the characteristic band minimum noise separation processing was 60%. By comparison, the overall recognition effect of the principal component analysis is better than the overall recognition effect of the minimum noise separation process; PCA and MNF are performed on the preferred characteristic bands, and the overall recognition rate is improved to 93.8% and 60%, respectively; therefore, The PCA of the characteristic wave band can effectively identify the defective fresh apricot and the normal fresh apricot. and (6) in order to further improve the detection rate of the fresh apricot defect, try to carry out the band ratio calculation of the image of the fresh apricot which is not recognized after the secondary main component analysis and detection. two combinations of the corresponding images in four feature bands are performed, and the 570nm/ 891nm wave band is selected to be detected than the image. The results showed that the identification rate of fresh apricot was increased from 80% to 88. 3%.
【學(xué)位授予單位】：石河子大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;S662.2
[Abstract]:Apricot is one of Xinjiang's characteristic fruits, its nutrient content is high and can be used for medical use, which is favored by consumers. The classification of fresh apricot is the key link of post-treatment, among which the surface defect of fresh apricot is one of the important indexes of classification. At present, the processing links of fresh apricot and apricot products lack mature automatic detection and classification technology, and mainly rely on the sorting of fresh apricot defect artificially, the efficiency is low, the labor intensity is large, and the quality is difficult to control. Because hyperspectral imaging technology combines the image and the spectrum into one feature, this paper makes use of this technique to detect the surface defects of fresh apricot. In this paper, Xinjiang fresh apricot is selected as the research object, and based on the visible/ near-infrared hyperspectral imaging technology, the normal fresh apricot, the fresh apricot, the mildewed fresh apricot and the insect-wound fresh apricot are detected by the visible/ near-infrared hyperspectral imaging technology, A nondestructive testing method for quickly and effectively identifying normal fresh apricot and defective fresh apricot is studied, which lays a theoretical foundation for constructing multi-spectrum on-line detection system suitable for fresh apricot defect. The main methods and research results are as follows: (1) the specific parameters of the fresh apricot image are determined, the high-spectrum images of four different types of fresh apricot are collected, the region of interest (ROI) of the fresh apricot image is manually extracted, the corresponding spectral data in each band is obtained. (2) The original spectral data of different kinds of fresh apricot were processed by S-G convolution smoothing, standard positive-state variable (SNV) and multi-scatter correction (MSC). The effects of three pretreatment methods on SVM modeling were compared. The results show that the support vector machine model of C-SVC type established by the original spectrum and S-G convolution smoothing pre-processing is better, and the recognition rate is 93. 3%. (3) Using PCA to reduce the spectral data of the whole band, the four characteristic bands of 495nm, 570nm, 729nm and 891nm are determined by weight coefficient. The classification and recognition effects of three different discrimination methods of support vector machine, partial least two multiplication discrimination and BP neural network are compared respectively in the full band and the characteristic band. The results show that SVM prediction model based on feature band construction is superior to SVM prediction model constructed by full band, and the recognition rate of C-SVC type SVM constructed by linear kernel function for the preferred feature band is 100%. The detection result of PLS-DA constructed by the whole band is superior to the detection discrimination result constructed by the characteristic wave band, and the BP neural network identification effect constructed by the whole band is superior to the BP neural network identification effect constructed by the characteristic band. In addition, the recognition effect of SVM based on feature band is better than that of BP neural network and PLS-DA. (4) carrying out full band main component analysis and characteristic band main component analysis on four different types of fresh apricot high-spectrum images, and selecting obvious PC image detection discrimination on the defect parts. The results showed that the classification accuracy of fresh apricot and moldy fresh apricot reached 100% with the analysis of the main components of the whole band. The discrimination rate of fresh apricot was 88. 3%, the recognition rate of fresh apricot was 38. 3%, and the main component analysis of the characteristic bands was used to detect and identify the fresh apricot. The recognition rate of fresh apricot and worm was 100%, 100%, 95% respectively, and the accuracy of discrimination of fresh apricot was improved to 80%. The overall recognition rate of the whole band principal component analysis was 81.7%, while the overall recognition rate of the characteristic band principal component analysis was improved to 93.8%. and (5) carrying out minimum noise separation conversion on the four different types of fresh apricot high-spectrum images on the basis of the whole band and the characteristic wave band, and selecting the MNF images with obvious defect parts to perform defect identification. The results showed that the recognition rate of fresh apricot and fresh apricot was 35.3% and 33.3% respectively, and the recognition rate of fresh apricot and fresh apricot was 53.3% and 50%, respectively. The recognition rate of fresh apricot and fresh apricot was improved to 73. 3% and 71.7%, the overall recognition rate of the whole band minimum noise separation processing was 43.8%, and the overall recognition rate of the characteristic band minimum noise separation processing was 60%. By comparison, the overall recognition effect of the principal component analysis is better than the overall recognition effect of the minimum noise separation process; PCA and MNF are performed on the preferred characteristic bands, and the overall recognition rate is improved to 93.8% and 60%, respectively; therefore, The PCA of the characteristic wave band can effectively identify the defective fresh apricot and the normal fresh apricot. and (6) in order to further improve the detection rate of the fresh apricot defect, try to carry out the band ratio calculation of the image of the fresh apricot which is not recognized after the secondary main component analysis and detection. two combinations of the corresponding images in four feature bands are performed, and the 570nm/ 891nm wave band is selected to be detected than the image. The results showed that the identification rate of fresh apricot was increased from 80% to 88. 3%.
【學(xué)位授予單位】：石河子大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;S662.2
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