基于机器视觉的不同类型甘蔗茎节识别

doi:10.11772/j.issn.1001-9081.2018092016

计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 1208-1213.DOI: 10.11772/j.issn.1001-9081.2018092016

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于机器视觉的不同类型甘蔗茎节识别

石昌友¹, 王美丽^1,2,3, 刘欣然¹, 黄慧丽¹, 周德强⁴, 邓干然⁵

1. 西北农林科技大学信息工程学院, 陕西咸阳 712100;
2. 农业农村部农业物联网重点实验室(西北农林科技大学), 陕西咸阳 712100;
3. 陕西省农业信息感知与智能服务重点实验室(西北农林科技大学), 陕西咸阳 712100;
4. 江南大学机械工程学院, 江苏无锡 214122;
5. 中国热带农业科学院农业机械研究所, 广东湛江 524091

收稿日期:2018-10-08 修回日期:2018-12-03 发布日期:2019-04-10 出版日期:2019-04-10
通讯作者: 王美丽
作者简介:石昌友(1984-),男,贵州松桃人,硕士研究生,主要研究方向:计算机视觉;王美丽(1982-),女,陕西杨凌人,副教授,博士,主要研究方向:计算机视觉、人工智能;刘欣然(1996-),女,河北邯郸人,主要研究方向:图像识别;黄慧丽(1996-),女,河南商丘人,主要研究方向:图像识别;周德强(1979-),男,湖北天门人,副教授,博士,主要研究方向:无损检测与自动化;邓干然(1972-),男,广西崇左人,研究员,主要研究方向:农机装备。
基金资助:
国家自然科学基金资助项目（41771315）。

Node recognition for different types of sugarcanes based on machine vision

SHI Changyou¹, WANG Meili^1,2,3, LIU Xinran¹, HUANG Huili¹, ZHOU Deqiang⁴, DENG Ganran⁵

1. College of Information Engineering, Northwest A & F University, Xianyang Shaanxi 712100, China;
2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs(Northwest A & F University), Xianyang Shaanxi 712100, China;
3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services(Northwest A & F University), Xianyang Shaanxi 712100, China;
4. School of Mechanical Engineering, Jiangnan University, Wuxi Jiangsu 214122, China;
5. Institute of Agricultural Machinery Research, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang Guangdong 524091, China

Received:2018-10-08 Revised:2018-12-03 Online:2019-04-10 Published:2019-04-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (41771315).

摘要/Abstract

摘要： 针对不同种类甘蔗表面多样性和复杂性等因素导致甘蔗图像的茎节难以识别问题，提出一种基于机器视觉且适合各种类型甘蔗的茎节识别方法。首先，通过迭代拟合法从原始图像中提取甘蔗目标区域，并估计甘蔗目标与横轴的倾斜角度，根据倾斜角度参数旋转甘蔗目标成近似平行横轴姿态；然后，利用双密度双树复小波变换（DD-DTCWT）对图像进行分解，使用不同层次的垂直和近似垂直方向的小波系数重构图像；最后，运用图像直线检测算法对重构图像进行检测，得到甘蔗茎节部位的边缘线，对边缘线的密度、长度、相互距离信息进一步验证便可实现甘蔗茎节的识别和定位。实验结果显示甘蔗茎节完整识别率达到92%，约80%的茎节的定位精度小于16个像素，95%的茎节的定位精度小于32个像素，所提方法在不同的图像背景下，都能够成功地对不同类型的甘蔗进行茎节识别，并且定位精度高。

关键词: 甘蔗茎节识别, 机器视觉, 双密度双树复小波变换, 直线检测算法

Abstract: The sugarcane node is difficult to recognize due to the diversity and complexity of surface that different types of sugarcane have. To solve the problem, a sugarcane node recognition method suitable for different types of sugarcane was proposed based on machine vision. Firstly, by the iterative linear fitting algorithm, the target region was extracted from the original image and its slope angle to horizontal axis was estimated. According to the angle, the target was rotated to being nearly parallel to the horizontal axis. Secondly, Double-Density Dual Tree Complex Wavelet Transform (DD-DTCWT) was used to decompose the image, and the image was reconstructed by using the wavelet coefficients that were perpendicular or approximately perpendicular to the horizontal axis. Finally, the line detection algorithm was used to detect the image, and the lines near the sugarcane node were obtained. The recognition was realized by further verifying the density, length and mutual distances of the edge lines. Experimental results show that the complete recognition rate reaches 92%, the localization accuracy of about 80% of nodes is less than 16 pixels, and the localization accuracy of 95% nodes is less than 32 pixels. The proposed method realizes node recognition for different types of sugarcane under different background with high position accuracy.

Key words: sugarcane node recognition, machine vision, Double-Density Dual Tree Complex Wavelet Transform (DD-DTCWT), line detection algorithm

中图分类号:

TP391.4

石昌友, 王美丽, 刘欣然, 黄慧丽, 周德强, 邓干然. 基于机器视觉的不同类型甘蔗茎节识别[J]. 计算机应用, 2019, 39(4): 1208-1213.

SHI Changyou, WANG Meili, LIU Xinran, HUANG Huili, ZHOU Deqiang, DENG Ganran. Node recognition for different types of sugarcanes based on machine vision[J]. Journal of Computer Applications, 2019, 39(4): 1208-1213.

参考文献

[1] 韦巧, 杨宝玲, 高振江.我国甘蔗产业化现状浅析[J]. 农机化研究, 2015, 37(4):247-253. (WEI Q, YANG B L, GAO Z J. Analysis of current situation of sugar cane industry[J]. Journal of Agricultural Mechanization Research, 2015, 37(4):247-253.)
[2] MOSHASHAI K, ALMASI M, MINAEI S, et al. Identification of sugarcane nodes using image processing and machine vision technology[J]. International Journal of Agricultural Research, 2008, 3(5):357-364.
[3] 陆尚平, 文友先, 葛维, 等.基于机器视觉的甘蔗茎节特征提取与识别[J]. 农业机械学报, 2010, 41(10):190-194. (LU S P, WEN Y X, GE W, et al. Recognition and features extraction of sugarcane nodes based on machine vision[J]. Transactions of the Chinese Society of Agricultural Machinery, 2010, 41(10):190-194.)
[4] 乔曦. 基于计算机视觉的甘蔗种茎切割防伤芽系统的研究[D]. 南宁:广西大学, 2013:24-36. (QIAO X. System of sugarcane cutting device to protect the bud based on computer vision[D]. Nanning:Guangxi University, 2013:24-36.)
[5] 黄亦其, 乔曦, 唐书喜, 等. 基于Matlab的甘蔗茎节特征分布定位与试验[J]. 农业机械学报, 2013, 44(10):93-97. (HUANG Y Q, QIAO X, TANG S X, et al. Localization and test of characteristics distribution for sugarcane internode based on Matlab[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(10):93-97.)
[6] 张卫正, 董寿银, 齐晓祥, 等. 基于图像处理的甘蔗茎节识别与定位[J]. 农机化研究, 2016, 38(4):217-221. (ZHANG W Z, DONG S Y, QI X X, et al. The identification and location of sugarcane internode based on image processing[J]. Journal of Agricultural Mechanization Research, 2016, 38(4):217-221.)
[7] KINGSBURY N. Image processing with complex wavelets[J]. Philosophical Transactions of the Royal Society A:Mathematical, Physical and Engineering Sciences, 1999, 357(1760):2543-2560.
[8] SELESNICK I W. The double-density dual-tree DWT[J]. IEEE Transactions on Signal Processing, 2004, 52(5):1304-1314.
[9] SELESNICK I W, BARANIUK R G, KINGSBURY N. The dual-tree complex wavelet transform[J]. IEEE Signal Processing Magazine, 2005, 22(6):231-239.
[10] 黄学优, 张长江. 双树复小波域的MRI图像去噪[J]. 中国图象图形学报, 2016, 21(1):104-113. (HUANG X Y, ZHANG C J. MRI denoising based on dual-tree complex wavelet transform[J]. Journal of Image and Graphics, 2016, 21(1):104-113.)
[11] CHEN S, QIU B, ZHAO F, et al. Fast compressed sensing MRI based on complex double-density dual-tree discrete wavelet transform[J]. International Journal of Biomedical Imaging, 2017, 2017:9604178.
[12] YASIN A S, PAVLOVA O N, PAVLOV A N. Speech signal filtration using double-density dual-tree complex wavelet transform[J]. Technical Physics Letters, 2016, 42(8):865-867.
[13] HU H F. Illumination invariant face recognition based on dual-tree complex wavelet transform[J]. IET Computer Vision, 2015, 9(2):163-173.
[14] KRISHNA M H, KUMAR G S. A noise based hybrid thresholding for enhanced noise reduction using double density dual tree complex wavelet transform[C]//Proceedings of the 2015 13th International Conference on Electromagnetic Interference and Compatibility. Piscataway, NJ:IEEE, 2015:208-211.
[15] ZHANG Y, BI S. Quantum-inspired remote sensing image denoising with double density dual-tree complex wavelet transform[C]//Proceedings of SPIE 9299. Bellingham, WA:SPIE Press, 2014:199-229.
[16] 武士想, 尚鹏, 王立功. 小波-Lagrange方法进行医学图像层间插值[J]. 中国图象图形学报, 2016, 21(1):78-85. (WU S X, SHANG P, WANG L G. Inter-slice interpolation for medical images by using the wavelet-Lagrange method[J]. Journal of Image and Graphics, 2016, 21(1):78-85.)
[17] von GIOI R G, JAKUBOWICZ J, MOREL J M, et al. LSD:a fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4):722-732.
[18] BURNS J B, HANSON A R, RISEMAN E M. Extracting straight lines[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 8(4):425-455.
[19] AKINLAR C, TOPAL C. EDLines:a real-time line segment detector with a false detection control[J]. Pattern Recognition Letters, 2011, 32(13):1633-1642.
[20] SALAVN Y, MARLET R, MONASSE P. The multiscale line segment detector[C]//RRPR 2016:Proceedings of the 2016 Reproducible Research in Pattern Recognition, LNCS 10214. Berlin:Springer, 2016:167-178.
[21] WEI H, YANG C, YU Q. Contour segment grouping for object detection[J]. Journal of Visual Communication and Image Representation, 2017, 48:292-309.
[22] TANG G, XIAO Z, LIU Q, et al. A novel airport detection method via line segment classification and texture classification[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(12):2408-2412.
[23] IZADI M, SAEEDI P. Three-dimensional polygonal building model estimation from single satellite images[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(6):2254-2272.
[24] BAE G Y, HA J M, JEON J Y, et al. LED traffic sign detection using rectangular Hough transform[C]//Proceedings of the 2014 International Conference on Information Science & Applications. Piscataway, NJ:IEEE, 2014:1-4.

基于机器视觉的不同类型甘蔗茎节识别

Node recognition for different types of sugarcanes based on machine vision

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张亚洲, 卢先领. 基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法[J]. 计算机应用, 2020, 40(5): 1545-1552.
[2]	刘子威, 邓春华, 刘静. 基于非对称沙漏网络结构的目标检测算法[J]. 计算机应用, 2020, 40(12): 3526-3533.
[3]	郜明, 任德均, 胡云起, 付磊, 邱吕. 基于机器视觉和轻量级卷积网络的安瓿瓶包装质量检测算法[J]. 计算机应用, 2020, 40(10): 2899-2903.
[4]	杨继东, 孙兆琦, 王飞龙. 基于视觉抓取的并联桁架机器人最优路径控制[J]. 计算机应用, 2019, 39(3): 913-917.
[5]	刘保成, 朴燕, 唐悦. 基于时空正则化的视频序列中行人的再识别[J]. 计算机应用, 2019, 39(11): 3216-3220.
[6]	张雲轲, 刘丹. 基于小型Zynq SoC硬件加速的改进TINY YOLO实时车辆检测算法实现[J]. 计算机应用, 2019, 39(1): 192-198.
[7]	李肖, 葛宝臻, 罗其俊, 李云鹏, 田庆国. 自由双目立体视觉摄像机动态外参数的获取[J]. 计算机应用, 2017, 37(10): 2888-2894.
[8]	赵树言, 路艳雪, 韩晓霞. 基于最近邻域像素梯度的视频背景快速提取[J]. 计算机应用, 2016, 36(8): 2322-2326.
[9]	李杰, 周浩, 张晋, 高赟. 基于粒子群优化的模板匹配跟踪算法[J]. 计算机应用, 2015, 35(9): 2656-2660.
[10]	宦海, 黄凌霄, 张雨, 卢松. 基于最大内切圆的椭圆孔组检测[J]. 计算机应用, 2015, 35(4): 1101-1105.
[11]	江杰周丽娜李刚. 基于机器视觉的羊体体尺测量[J]. 计算机应用, 2014, 34(3): 846-850.
[12]	徐霄王润彭国杰杨琦王忆文李辉. 机器视觉电子白板的校准实现技术[J]. 计算机应用, 2014, 34(1): 139-141.
[13]	周祖微刘森王忆文李辉. 机器视觉电子白板系统的防抖与坐标插值技术[J]. 计算机应用, 2012, 32(12): 3408-3410.
[14]	高直朱志浩徐永红洪文学. 基于四元数小波变换及多分形特征的纹理分类[J]. 计算机应用, 2012, 32(03): 773-776.
[15]	倪松鹏王晓年朱劲. 用于烟箱缺条检测的禁忌匹配算法[J]. 计算机应用, 2012, 32(01): 269-271.