纺织学报

• 纺织工程 • 上一篇    下一篇

籽棉中异性纤维的双光源成像检测方法

  

  • 出版日期:2017-04-15 发布日期:2017-04-15

Detecting method of foreign fibers in seed cotton using double illumination imaging

  • Online:2017-04-15 Published:2017-04-15

摘要:

针对现有的异纤清理机无法彻底清除异性纤维的问题,提出了一种在籽棉轧花前对异性纤维进行检测的方法。以清除了铃壳、茎、叶等有机杂物的籽棉和常见的21种有色及白色异纤为检测样本,在白色LED和红色线激光双光源照明获取图像,在RGB颜色空间的R 通道和HSI颜色空间的S通道利用改进的索贝尔(Sobel)边缘检测算法检测异纤。同时在S通道利用一维最大熵法以提高异纤检测率。实验结果表明:采用的双光源照明成像方法和图像处理算法可减少阴影等干扰,白色异纤的检出率可达到74.7%,有色异纤的检出率可达到70.8%,为籽棉中异性纤维的检测提供了参考和借鉴。

关键词: 籽棉, 异性纤维, 双光源成像, 颜色空间, 图像处理

Abstract:

Aiming to improve the efficiency of detection the foreign fibers, a new approach of detecting foreign fibers in cleaned seed cotton before the ginning stage was proposed. In the experiment, cleaned seed cotton, in which organic impurities such as boll shells, stems and leaves were removed, and 21 kinds of common white or color foreign fibers were used as detection samples. Images were acquired under the double illmination of white LED and red line-laser. Then, an improved Sobel edge edtection algorithm was used in the Red channel of RGB color space and the Saturation channel in HSI color space separately. And a one-dimension maximum entropy thresholding method was also implemented in the Saturation channel for increasing the successful detecting rate. Expeiment results indicate that the double illumination imaging and the image processing algorithm reduce interference such as shadows. The successful detecting rates of white and color foreign fibers are up to 74.7% and 70.8%, respectively. This paper provides a reference for detecting forgign fibers in seed cotton.

Key words: seed cotton, foreign fiber, double illumination imaging, color space, image processing

[1] 陆奕辰 王蕾 唐千惠 潘如如 高卫东. 应用图像处理的纱线黑板毛羽量检测与评价[J]. 纺织学报, 2018, 39(08): 144-149.
[2] 王雯雯 高畅 刘基宏. 应用卷积神经网络的细纱断纱锭位识别[J]. 纺织学报, 2018, 39(06): 136-141.
[3] 何晓昀 韦平 张林 邓斌攸 潘云峰 苏真伟. 基于深度学习的籽棉中异性纤维检测方法[J]. 纺织学报, 2018, 39(06): 131-135.
[4] 王雯雯 刘基宏. 应用优化霍夫变换的细纱断头检测[J]. 纺织学报, 2018, 39(04): 36-41.
[5] 王传桐 胡峰 徐启永 吴雨川 余联庆. 改进频率调谐显著算法在疵点辨识中的应用[J]. 纺织学报, 2018, 39(03): 154-160.
[6] 牟新刚 蔡逸超 周晓 陈国良. 基于机器视觉的筒子纱缺陷在线检测系统[J]. 纺织学报, 2018, 39(01): 139-145.
[7] 王晓予 向军 潘如如 梁惠娥 高卫东. 服饰刺绣图案的自动提取与色块分割[J]. 纺织学报, 2017, 38(09): 120-126.
[8] 张继东 薛元 张杰 郭明瑞 魏晓婷 高卫东 . 应用混色纱纹理信息的纬编针织物模拟[J]. 纺织学报, 2017, 38(07): 148-154.
[9] 路凯 钟跃崎 朱俊平 柴新玉. 基于视觉词袋模型的羊绒与羊毛快速鉴别方法[J]. 纺织学报, 2017, 38(07): 130-134.
[10] 张宁 李忠健 潘如如 高卫东 韩要宾. 采用色纺纱图像的真实感色织物模拟[J]. 纺织学报, 2017, 38(05): 37-42.
[11] 刘成霞 韩永华. 模拟实际着装的织物抗皱性测试方法[J]. 纺织学报, 2017, 38(03): 56-60.
[12] 王传桐 胡峰 徐启永 吴雨川 余联庆. 采用Gabor滤波簇和等距映射算法的织物疵点检测方法[J]. 纺织学报, 2017, 38(03): 162-167.
[13] 李冠志 赵强 汪军 Gong Hugh . 纱线截面压缩变形仿真与验证[J]. 纺织学报, 2017, 38(02): 184-190.
[14] 李忠健 潘如如 高卫东. 应用纱线序列图像的电子织物构建[J]. 纺织学报, 2016, 37(3): 35-0.
[15] 韩蓉 胡堃 毋戈 钟跃崎. 应用图像法的织物弯曲刚度计算[J]. 纺织学报, 2016, 37(3): 41-0.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!