| 基于颜色和形状特征的机采棉杂质识别方法 |
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| 引用本文： | 张成梁,李 蕾,董全成,葛荣雨.基于颜色和形状特征的机采棉杂质识别方法[J].农业机械学报,2016,47(7):28-34. |
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| 作者姓名： | 张成梁 李 蕾 董全成 葛荣雨 |
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| 作者单位： | 济南大学,齐鲁工业大学,济南大学,济南大学 |
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| 基金项目： | 国家自然科学基金项目（51305164、51405194） |
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| 摘 要： | 机采棉的含杂识别分类检测能够提高棉花加工设备效率,减少棉花纤维损伤,并为棉花收获设备的改进提供指导。提出了一种基于颜色和形状特征的机采棉杂质识别分类方法,对大杂质和小杂质检测采取不同的图像处理方法。颜色特征主要采用基于彩色梯度图像的分水岭变换与改进模糊C均值聚类方法融合的方法;形状特征主要采用机采棉杂质的面积、周长、离心率和矩形度特征。通过对100幅机采棉图像试验表明,该方法对各类杂质的平均识别正确率为89%。
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| 关 键 词： | 机采棉 颜色特征 形状特征 杂质识别 分水岭 改进模糊C均值聚类 |
| 收稿时间： | 1/6/2016 12:00:00 AM |
Recognition Method for Machine-harvested Cotton Impurities Based on Color and Shape Features |
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| Zhang Chengliang,Li Lei,Dong Quancheng and Ge Rongyu.Recognition Method for Machine-harvested Cotton Impurities Based on Color and Shape Features[J].Transactions of the Chinese Society of Agricultural Machinery,2016,47(7):28-34. |
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| Authors: | Zhang Chengliang Li Lei Dong Quancheng and Ge Rongyu |
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| Institution: | University of Jinan,Qilu University of Technology,University of Jinan and University of Jinan |
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| Abstract: | The type and content of the impurities in machine harvested cotton are important parts of the cotton parameters, and they determine the adjustment of the processing technique of cotton. A method based on color and shape features for recognition of machine harvested cotton impurities was presented. Different image processing methods were adopted for large impurities and small impurities, and the detailed algorithm flow chart was formulated. The window filtering, image segmentation, color feature statistics and shape feature extraction were adopted to process image. For the large impurities image, smooth filtering, clustering segmentation, binarization, hole filling were conducted sequentially, and then, shape features such as area, perimeter, eccentricity, rectangle degree and color pixel statistics of the target region were calculated. The impurities which include branches, boll shell, stiff flap and leaf were identified by using combination of color and shape features. For the small impurities image, large and yellow impurities were removed after image sharpening and clustering segmentation, and the area was calculated through color pixel statistics. In order to speed up the calculation and improve the recognition rate, watershed algorithm based on color gradient image and improved fuzzy C means clustering algorithm with specified initial cluster centers were combined to split image. As a result, the recognition and classification of machine harvested cotton impurities can increase the efficiency of cotton processing equipment, reduce damage of cotton fiber, and provide improved guidance for cotton harvest equipment. For the investigated 100 sample images including five types of cotton impurities, a 89% successful recognition rate was achieved. |
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| Keywords: | machine harvested cotton color feature shape feature impurities recognition watershed algorithm improved fuzzy C means clustering algorithm |
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