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当前位置: 首页 > 文章 > 基于选择性注意机制的果实簇识别与采摘顺序规划 农业机械学报 2016,47 (11) 1-7
Position: Home > Articles > Fruit Cluster Recognition and Picking Sequence Planning Based on Selective Attention Transactions of the Chinese Society for Agricultural Machinery 2016,47 (11) 1-7

基于选择性注意机制的果实簇识别与采摘顺序规划

作  者：
王冰心;王孙安;于德弘
单  位：
西安交通大学机械工程学院
关键词：
采摘机器人;选择性注意;目标识别;顺序规划
摘  要：
基于仿生学思想设计了一种果实簇识别与采摘顺序规划方法。该方法以Itti视觉注意基本模型为基础,通过改进视觉特征整合方法,构建依赖先验知识的视觉显著图,改善果实簇的识别效果;借鉴"赢者取全"的生物神经竞争机制和采摘专业人员的操作经验,设计距离、面积和显著度加权择优的采摘顺序规划策略,提高采摘工作效率。试验结果表明,设计的算法能够识别20余种常见果蔬,识别正确率达到93.36%;果实簇的采摘顺序规划结果符合人工采摘的专家经验。
译  名：
Fruit Cluster Recognition and Picking Sequence Planning Based on Selective Attention
作  者：
Wang Bingxin;Wang Sun'an;Yu Dehong;School of Mechanical Engineering,Xi'an Jiaotong University;
关键词：
harvesting robot;;selective attention;;target recognition;;sequence planning
摘  要：
To improve algorithm versatility and picking efficiency,recognizing more kinds of fruits or vegetables and planning picking sequence for fruit clusters in visual field have become hotspots and trends of harvesting robot research. Primates can find objects and shift attention through visual selective attention mechanism,which has similarities with harvesting robots to recognize targets and to plan picking sequence. Hence,by adopting the idea and technology of bionics,a new visual selective attention-based method was proposed for fruit cluster recognition and picking sequence planning. According to Itti visual attention computational model,this algorithm changed computing process for color feature maps,and improved feature integration method for building color conspicuity map and saliency map by introducing priori knowledge about fruits and vegetables. Using the reference of the biological neural network competition mechanism called Winner-Take-All and artificial expertise from professional picking operators,distance,area and saliency were adopted to design the weighted preferential picking sequence strategy for fruit clusters. The experimental results showed that the proposed method in this paper achieved recognition of more than 23 kinds of familiar fruits and vegetables. Recognition correctness was higher than 93. 36%. In addition,the results of picking sequence planning showed that its planning way was consistent with artificial expertise from manual picking operation.

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