Abstract
Apple’s typical quality detection experiment was conducted on visual detection platform and the development environment Halcon in this paper. After image acquisition and processing the region of interest was extracted to find out the specific area containing the apple. Moreover, its contour was extracted using dynamic threshold segmentation method, and the area of this contour was calculated and used to assess the size of the apple. Furthermore, the typical defect fruit rot was detected using threshold segmentation method, and then the different gray values were shown in different colors. Based on different region’s colors, the defects were detected based on the colors of different regions, and the color feature was valued using RGB color model and template matching method. A good region was selected to create a template and set parameters, and then the appropriate threshold was set as a qualified color according to the standard color of the R component. In this study, the extraction and the area of the fruit contour were calculated, and the defect and color detections of apples were realized. Finally, good results in the size, defect and color detections of apples were achieved by using Halcon and machine vision methods.
Publication Date
10-28-2016
First Page
123
Last Page
126
DOI
10.13652/j.issn.1003-5788.2016.10.030
Recommended Citation
Huiyu, XIANG; Zhen, XUE; Chongjie, LENG; and Jing, HU
(2016)
"Experimental study of apple quality vision detection based on Halcon,"
Food and Machinery: Vol. 32:
Iss.
10, Article 29.
DOI: 10.13652/j.issn.1003-5788.2016.10.030
Available at:
https://www.ifoodmm.cn/journal/vol32/iss10/29
References
[1] 胡发焕, 董增文, 匡以顺. 基于机器视觉的脐橙品质在线分级检测系统[J]. 中国农业大学学报, 2016, 21(3): 112-118.
[2] 刘静, 黄勇平, 章程辉. 视觉系统开发模块在芒果果面缺陷检测中的应用[J]. 食品与机械, 2009, 25(2): 82-85.
[3] 游家训, 陈超, 郭创新. 径向基神经网络在水果颜色分级机中的应用[J]. 轻工机械, 2008, 26(3): 24-26.
[4] 张俊雄, 荀一, 李伟. 山竹的计算机视觉分级方法[J]. 农业机械学报, 2009, 40(11): 176-179, 188.
[5] BLASCO J, ALEIXOS N, MOLT E. Machine vision system for automatic quality grading of fruit [J]. Biosystems Engineering, 2003, 85(4): 415-423.
[6] 黄星奕, 魏海丽, 赵杰文. 实时在线检测苹果果形的一种计算方法[J]. 食品与机械, 2006, 22(1): 27-29.
[7] 黎移新. 基于前馈神经网络的脐橙糖度及有效酸度检测[J]. 食品与机械, 2009, 25(2): 78-81.
[8] 王宏丽, 赵不贿, 孙智权, 等. 基于HALCON的医疗袋缺陷检测[J]. 包装工程, 2015, 36(13): 125-129.
[9] 刘娟娟. 基于红外和可见光图像融合的苹果缺陷检测[D]. 南昌: 华东交通大学, 2012: 29-33.
[10] STEGER C. 机器视觉算法与应用[M]. 杨少荣, 译. 北京: 清华大学出版社, 2008: 144-149.
[11] 闫之烨. 基于计算机视觉的苹果颜色分级系统的研究[D]. 南京: 南京农业大学, 2003: 8-10.
[12] 应义斌, 王剑平, 蒋焕煜. 水果直径和缺陷面积的机器视觉检测(英文)[J]. 农业工程学报, 2002, 18(5): 216-220.
[13] 刘佳男. 基于机器视觉的水果表面缺陷识别方法的研究[D]. 无锡: 江南大学, 2012: 49-50.