李立君,李昕,高自成,周健,闵淑辉.基于偏好免疫网络的油茶果采摘机器人图像识别算法[J].农业机械学报,2012,43(11):209-213.
Li Lijun,Li Xin,Gao Zicheng,Zhou Jian,Min Shuhui.Camellia Fruit Image Recognition Based on Preference Artificial Immune Net[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(11):209-213.
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基于偏好免疫网络的油茶果采摘机器人图像识别算法   [下载全文]
Camellia Fruit Image Recognition Based on Preference Artificial Immune Net   [Download Pdf][in English]
  
DOI:10.6041/j.issn.1000-1298.2012.11.039
中文关键词:  油茶果  采摘机器人  偏好人工免疫网络  图像识别
基金项目:林业公益性行业科研专项基金资助项目(201104090)
作者单位
李立君 中南林业科技大学 
李昕 中南林业科技大学 
高自成 中南林业科技大学 
周健 中南林业科技大学 
闵淑辉 中南林业科技大学 
中文摘要:针对油茶果采摘机器人视觉识别中外界物体的形态学特性要求,采用了偏好人工免疫网络算法作为机器视觉的图像识别算法,并根据采摘环境及采摘对象的特点对算法结构进行了改进,增强了算法的识别率。仿真实验表明,采用偏好人工免疫网络算法对油茶果的识别率在晴天时达到了81.67%,阴天时达到了87.69%,满足采摘识别率的要求。
Li Lijun  Li Xin  Gao Zicheng  Zhou Jian  Min Shuhui
Central South University of Forestry and Technology;Central South University of Forestry and Technology;Central South University of Forestry and Technology;Central South University of Forestry and Technology;Central South University of Forestry and Technology
Key Words:Camellia fruit  Picking robot  Preference artificial immune net  Image recognition
Abstract:With the demanding of morphological features recognition in picking robot machine-vision system, preference artificial immune net (aiNet) was used as the image recognition algorithm, mainly modified the structure of the algorithm to promote the accuracy rate. The algorithm was modified according to picking environment and real-time require. The simulation proved the clustering accuracy of preference aiNet reached to 81.67% in the sunny day and 87.69% in the cloudy day. The modified algorithm has certain meaning in the next research of picking robot.

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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