陈金星,岳德鹏,冯仲科,丁家巍,姚炳全,叶添雄.树干直径自动识别与测量技术研究[J].农业机械学报,2016,47(3):349-353.
Chen Jinxing,Yue Depeng,Feng Zhongke,Ding Jiawei,Yao Bingquan,Ye Tianxiong.Automatic Recognition and Measurement Technology of Tree Trunk Diameter[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):349-353.
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树干直径自动识别与测量技术研究   [下载全文]
Automatic Recognition and Measurement Technology of Tree Trunk Diameter   [Download Pdf][in English]
投稿时间:2015-11-18  
DOI:10.6041/j.issn.1000-1298.2016.03.049
中文关键词:  树干直径  Canny边缘检测算法  自动识别  图像识别  测量
基金项目:国家林业局引进国际林业科学技术项目(2014-4-76)和国家自然科学基金项目(41371189)
作者单位
陈金星 北京林业大学 
岳德鹏 北京林业大学 
冯仲科 北京林业大学 
丁家巍 常州市新瑞得仪器有限公司 
姚炳全 北京林业大学 
叶添雄 北京林业大学 
中文摘要:在森林资源调查中为实现树干直径快速、精确测量,使用电子元件进行了树干影像自动识别与测量。通过摄像头和CMOS传感器获得图像,经灰度转换后,使用窗口大小为5行5列的高斯滤波器,利用Canny边缘检测算法提取边缘,包括平滑、非极大值剔除、双阈值的边缘连接等,在提取的边缘图像基础上,设计了树木直径自动提取算法。以4列的窗口为模板,自上而下,对经过Canny算法后的图像进行竖直线段的提取。在提取所有竖线后,将最大宽度的2条竖线当作树干轮廓线,通过焦距、物距、像距与像素宽度之间的关系计算出树干直径。最后选择了不同树种共计16棵树木进行了验证,实验结果表明树干直径识别的精度为96.9%,绝大部分测量数据符合森林资源调查要求。
Chen Jinxing  Yue Depeng  Feng Zhongke  Ding Jiawei  Yao Bingquan  Ye Tianxiong
Beijing Forestry University,Beijing Forestry University,Beijing Forestry University,Changzhou New Ruide Instrument Co., Ltd.,Beijing Forestry University and Beijing Forestry University
Key Words:tree trunk diameter  Canny edge detection algorithm  automatic recognition  image recognition  measurement
Abstract:Tree trunk diameter is one of the most important tree measuring factors in forest inventory. To quickly and accurately measure the tree trunk, electronic components were applied for tree trunk image recognition and measurement. Application of image processing technology in forest mensuration proposes a solution for accurately measuring trees, which makes non professional technicians measure trees easily without experience. Images were taken by camera and processed by CMOS. The image was smoothed by Gaussian inverse filter after conversion from RGB to greyscale. Then edge was detected through non maximum suppression and double thresholds edge connection. Tree trunk automatic detection algorithm was developed on the base of the detecting image. The algorithm used a 4 column window that represented a vertical segment to extract the vertical segments from the images above. The algorithm got rid of the vertical segment with over two successive 0 values, including vertical, horizontal and diagonal directions. The detected 0 value was searched from top to bottom. In addition, individual points in the window were removed. When all the vertical lines were abstracted, the two vertical lines with the maximum width were represented as the tree trunk. Tree trunk diameter was computed according to the relationship among focal length, object distance, image distance and pixel size. The image recognition results were validated by selecting different trees. Results showed that the image recognition precision was 96.9% and most data were conformed to the requirement of forest inventory. The forestry intelligence was explored and the digitizing components was used to realize the forestry intelligence.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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