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吴兰兰,徐恺,熊利荣.基于视觉注意模型的苗期油菜田间杂草检测[J].华中农业大学学报,2018,37(2):96-102
基于视觉注意模型的苗期油菜田间杂草检测
Detecting weed in seedling rapeseed oil field based on visual-attention model
投稿时间:2017-05-11  
DOI:
中文关键词:  Itti模型  显著图  图像分割  支持向量机  大田油菜  杂草识别
英文关键词:Itti model  saliency map  image segmentation  SVM  seedling rapeseed oil field  weed detection
基金项目:国家自然科学基金项目(31401288);中央高校基本科研业务费专项(2662015PY078)
作者单位E-mail
吴兰兰 1.华中农业大学工学院武汉 430070 2.华中农业大学农业部长江中下游农业装备重点实验室武汉 430070 wulanlan@mail.hzau.edu.cn 
徐恺 华中农业大学工学院武汉 430070  
熊利荣 华中农业大学工学院武汉 430070 bearlgj@webmail.hzau.edu.cn 
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中文摘要:
      提出了基于视觉注意模型的苗期油菜/杂草图像检测方法。针对苗期油菜大田环境,获取油菜/杂草RGB原始图像。根据原始图像颜色分布特点改进Itti模型,生成系列特征显著图,结合区域生长算法分割出感兴趣区域。针对该区域提取形状和纹理特征参数作为支持向量机输入量,判别出所有油菜区域,最后融合原始图像和油菜区域获取最终株间杂草区域。结果表明:与局部迭代阈值法和最大类间方差法相比,本研究提出的图像分割方法更优,正确分割目标概率、错误分割目标概率及漏分割目标概率分别为92.46%、3.26%及7.54%;针对形状、纹理、综合特征及精选特征四类特征参数集,径向基-支持向量机的识别率分别为96.00%、94.29%、100.00%及96.00%。
英文摘要:
      A new rapeseed oil seedling/weed detection method based on visual-attention model was put forward.The RGB images for the rapeseed oil seedling and weed in the seedling rapeseed oil field were obtained.Series of feature saliency using an improved Itti model in terms of the distribution characteristics of the original images were mapped.The ROI using region growing method were extracted.We calculated the shape and texture feature parameters of the regions segmented before and put them as the input of SVM used to identify seedling rapeseed oil regions.The weed regions were obtained by combining the original images with the seedling rape regions using a logical operation.The results showed that the correct segmentation rate,false segmentation rate and error segmentation rate of the proposed method was 92.46%,3.26% and 7.54%,respectively.It is indicated that the proposed method is better than the other two image segmentation methods.Using shape,texture,comprehensive and specifically selected feature parameters as the input,the classification rate of RBF-SVM was 96.00%,94.29%,100.00% and 96.00%,respectively.
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