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.
Zhang Chengliang,Li Lei,Dong Quancheng,Ge Rongyu.Recognition Method for Machine-harvested Cotton Impurities Based on Color and Shape Features[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):28-34.
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基于颜色和形状特征的机采棉杂质识别方法   [下载全文]
Recognition Method for Machine-harvested Cotton Impurities Based on Color and Shape Features   [Download Pdf][in English]
投稿时间:2016-01-06  
DOI:10.6041/j.issn.1000-1298.2016.07.005
中文关键词:  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
基金项目:国家自然科学基金项目(51305164、51405194)
作者单位
张成梁 济南大学 
李 蕾 齐鲁工业大学 
董全成 济南大学 
葛荣雨 济南大学 
中文摘要: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%。
Zhang Chengliang  Li Lei  Dong Quancheng  Ge Rongyu
University of Jinan,Qilu University of Technology,University of Jinan and University of Jinan
Key Words:machine harvested cotton  color feature  shape feature  impurities recognition  watershed algorithm  improved fuzzy C means clustering algorithm
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.

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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