| Shape is one of the key quality characteritcs of fruits. But, it is very difficult to describe the fruit shape beacause of its big difference among natural morphological characteristics of different fruits. The existing shape description methods mostly are facing many problems: complex executing, special fruits’postures, unilateral detection and low accuracy. They are not suitable for shape detection of pose-varied fruits on the high speed production line.In this paper, apples are mainly research objects. Shape description methods based on machine vision were studied from2D and3D images. The description methods of shape characteristics among different varieties of apples were studied, and the description methods of shape characteristics among different grades of a variety of apples were researched. A three cameras machine vision detection system was constructed, and fast and exact description of shape of pose-varied moving fruit was realized, and consistency of fruit shape discriminant under changing posture circumstances was guaranteed.The main research contents, results and conclusions were listed as follows:(1) A novel background segmentaion and target extraction method called FOFS was proposed. Using OTSU method directly cannot get a complete target because of influence of the fruit color, size, defects and illumination. In order to improve the background segmentation precision and adaptability to different fruits, the R, G, B companents of one color image were fusioned firstly, then the fusional image was denoised by morphological opening method, then followed by removing zigzag boundary through the spatial filtering, finally the background was segmented and the target was extracted by automatic threshold segmentation method.280apple images were processed by this method, and the results show that203image segmentation deviation is less than1%, accounting for72.50%of the total;70image segmentation deviation in1%～1%, accounting for25%of the total;7image deviation is greater than2%, accounting for2.50%of the total; the largest segmentation deviation is2.83%. The results indicate that the FOFS method is good. Segmentation deviation is less than2.83%, and the extraction edge is smooth, and it has good illumination adaptability. The FOFS method can better eliminate the influence of color, position, size, defects and stem calyx to background segmentation.(2) A3D shape description method of centroid distance deviation was presented. A2D image is only one of imaging plane of the apple, so shape detection by the description methods based on2D images was incomplete, and its accuracy was low.480images of3D points clouds of10varieties of apples by structured light3D imaging technology were analyzed by the method of centroid distance deviation. The results show that, for regular shape apples, the method of centroid distance deviation can distinguish slender, near slender, square and round. This results indicate that centroid distance deviation can describe shape features of slender, near slender, square and round.(3) Two dimensional shape description methods of symmetry index, rectangularity, contour irregularities, contour kurtosis and eccentricity were presented. Fourier descriptors, wavelet descriptors, Hu descriptors, Zernike moments descriptors,wavelet moment descriptors were improved. The traditional shape description indexes are inaccurate and they often have big deviation.so some new indexes were presented and some were improved by contrastive analysis with traditional indexes. The different shape grades of the same apple variety were detected by this method. The results showed that ten times of shape online detection of200apples by dispersion threshold of symmetry index Os proved the accuracy was about80.15%. And the distinguishing accuracy of the method of the improved Zernike moment Z2-0threshold was above66%. The results of the440apple images processing showed that the distinguishing accuracy of the method of the improved Zernike moment Z3-1threshold was above83.75%. The results of the34cylindrical apples and58spherical apples images processing showed that the distinguishing accuracy of the method of the improved wavelet moment W3-8-1threshold was above93.10%. This results indicate that symmetry index Os, improved Zernike moment and wavelet moment are useful.(4) A method of using Zernike moment Z3-1and Wavelet moment W3-8-1’s thresholds to classify the shape grade were proposed. In this paper,11varieties of apples were as research objects.244shape description indexes including circularity factor, complexity, occupancy, symmetry index, rectangularity, contour irregularities, contour kurtosis, eccentricity, roundness, circular ratio, the ratio of circumradius and Fourier descriptors, wavelet descriptors, Hu descriptors, Zernike moments descriptors, wavelet moment descriptors were analyzed. The results of440images processing show that the determining accuracy is above83.75%by setting Z3-1’s threshold12. greater than12cylindrical fruit, less than12spherical fruit. The determining accuracy is above93.10%to34groups of cylindrical and58groups of spherical fruits according to setting W3-8-1’s threshold5.5, greater than5.5clylindrical, less than5.5round. And using multiple linear regression model, its accuracy is56.52%. The results indicate that the Zernike moment Z3-1and Wavelet moment W3-8-1threshold method can recognize the shape of cylindrical and spherical. Its accuracy is higher than multiple liner regression model, and it has low computational complexity.(5) A method of using Zernike moment Z2-0’s threshold to judge deformation for all kinds of apple attitudes, and the method of eccentricity Ec, circularity factor C4, symmetry index Os, Zernike moment Z5-3’s thresholds for special attitudes apple images were proposed. Most researchers are focusing on shape description of special attitude fruit, and few care about shape description of multitude pose fruits. In this paper,29Fuji apples and36Jonah gold apples were picked from Qixia’ orchards in Shandong province. They were respectively selected to three shape grades according to their shape features by naked eyes. Each apple had8images from different attitudes.244shape description indexes including circularity factor, complexity, occupancy, symmetry index, rectangularity, contour irregularities, contour kurtosis, eccentricity, roundness, circular ratio, the ratio of circumradius and Fourier descriptors, wavelet descriptors, Hu descriptors, Zernike moments descriptors, wavelet moment descriptors were analyzed. The results of440images processing show that whatever any pose of apples, setting Z2-0’s threshold12, greater than12for distortionless, less than12for deformation, the detecting accuracy was about67.24%for Fuji apples and about43.06%for Jonah golden apples. And the detecting accuracy was only11.21%for Fuji apples and about30.09%for Jonah golden apples by multiple linear regression model. When fruit axis was leaned, the detecting accuracy of setting Ec’s threshold0.1was87.93%for Fuji apples and79.17%for Jonah golden apples. The detecting accuracy of setting C4’s threshold0.92was81.03%for Fuji apples and65.28%for Jonah golden apples. When fruit axis was vertical, the detecting accuracy of setting Ec’s threshold0.1was67.24%for Fuji apples and59.72%for Jonah golden apples. The detecting accuracy of setting C4’s threshold0.92was65.52%for Fuji apples and59.72%for Jonah golden apples. When fruit axis was horizonal, setting Os’s threshold0.9, greater than0.9for distortionless, less than0.9for deformation, the detecting accuracy was about73.28%for Fuji apples and about59.03%for Jonah golden apples. Setting Z5-3’s threshold11.9, greater than11.5for distortionless, less than11.5for deformation, the detecting accuracy was about70.69%for Fuji apples and about52.08%for Jonah golden apples. The results indicate that Zernike moment Z2-0can determine deformation. When fruit axis was vertical or leaned, eccentricity Ec and circularity factor C4can get better discrimination. When fruit axis was horizonal, symmetry index Os and Zernike moment Z5-3can get better discrimination. (6) A three camaras machine vision system was designed, and a method of using Zernike moment Z2-0’s threshold to judge deformation was proposed. And a method of using dispersion of circularity factor C4and symmetry index Os to judge deformation was presented. Most researchers are on shape description of fixed pose motionless fruit, and shape description of moving pose varied fruits is always difficult. From the fruits market, we bought Fuji apples that had3kinds of size specifications of90mm,80mm.70mm.5distortionless fruit and8deformational fruits were researched objects. And each apple must be tested for50times, and1950images were captured. According to the study on shape feature description methods of diffirent shape grades of the same apple variety, Zernike moment Z2-0, Z3-1, Z5-3and circularity factor C4, symmetry index Os, eccentricity Ec were analyzed mainly. The results show that setting Z2-0’s threshold13. greate than13for distortionless, less than13for deformation,3apples in5distortionless fruits were discriminated as distortionless fruits for50times tests and other2apples got the wrong results. And5apples in8deformational fruits were discriminated as deformation for50times tests and other apples had some wrong discrimination in some tests. The dispersion of circularity factor C4and symmetry index Os was calculated by statistic analysis method. Setting C4’s dispersion threshold10%, less than10%distortionless, when greater than10%. Os’s dispersion less than20%distortionless, other deformation, this method can completely discriminate the shape deformation or not. In validation tests,2kinds of distornless and deformation Fuji apples were selected. Each kind had100samples, and each sample must be tested for10times. And9imges of3postions for each time were captured, so18000images were obtained in total. The results of image processing show that, based on3camaras machine vision system, setting capturing3images from only1position, and, the accuracy of processing3images was about66%by Zernike moment Z2-0threshold12.7and average detection time for each apple was about0.2063seconds. Setting capturing9images from3positions, the accuracy of processing9images was about80.15%by dispersion threshold of symmetry index Os and average detection time for each apple was about1.649seconds. The results indicate that the methods of Zernike moment Z2-0threshold and dispersion threshold of symmetry index Os based on3camaras machine vision can discriminate the shape. The Zernike moment Z2-0threshold method has good real-time and the method of dispersion threshold of symmetry index Os has high accuracy. |
PDF Full Text Request