Design and Test of Machine Vision Inspection System for Cotton Preparation

被引：0
|
作者：
Xia B. [1 ]
Shi S. [1 ]
Zhang R. [2 ,3 ]
Qin J. [1 ]
Liu Y. [1 ]
Chang J. [2 ,3 ]
机构：
[1] Zhengzhou Cotton and Jute Engineering Technology and Design Research Institute, China CO - OP, Zhengzhou
[2] College of Mechanical and Electrical Engineering, Shihezi University, Shihezi
[3] Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 11期
关键词：
angular second moment; cotton preparation; defect; image segmentation; machine vision;
D O I：
10.6041/j.issn.1000-1298.2023.11.018
中图分类号：
学科分类号：
摘要：
Aiming at the problems of labor intensity, strong subjectivity and low detection efficiency in the current manual sensory inspection of cotton preparation, a machine vision-based cotton preparation inspection system was designed. The system consisted of cotton pressing mechanism, image acquisition mechanism, detection processor, detection control board and touch screen. Firstly, a low-angle direct lighting system and an image acquisition mechanism were designed, where the LED light source was illuminated at an angle of 45° to the normal of the inspection window, and the industrial camera collected cotton images through the optical glass. Then the system adopted image texture features to express the appearance morphology of cotton, and established a relationship model between image texture features and appearance morphology by measuring the angular second moment of cotton preparation sample standards. In the adaptive filtering and Canny algorithm, it integrated the noise point evaluation and the high and low threshold adaptive methods for image filtering and segmentation identification, and the ginning quality level determination was made according to the Euclidean distance. Finally, cotton samples were selected for system performance test verification. The results showed that the angluar second moment of the ginning quality physical standards PI, P2 and P3 were [0. 893 2, 1 ], [0. 689 1, 0. 776 1 ], [0. 213 6, 0. 587 3 ], respectively, and the difference in the texture eigenvalues of the angular second moment between the grades was obvious, which verified the feasibility of the image texture to express the appearance and morphology of cotton. The relative deviation of the inspection of the number of defects index of the system was 0. 15, and the separation effect of defects and background was obvious. Compared with the national standard inspection method, the detection accuracy of the preparation visual system reached 94.20%, and the detection deviation was not more than 1 preparation grade, which was in high consistency with the national standard inspection results. The detection time of single cotton sample system was 1.2 s, and the detection efficiency was improved by 77. 36% . The system can meet the requirements of field use, and provide a technical reference for the instrumental detection of cotton preparation indexes. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
引用
收藏
页码：189 / 197
页数：8
相关论文
共 26 条
  • [1] LU Xiuru, JIA Xiaoyue, NIU Jiahui, The present situation and prospects of cotton industry development in China, Seientia Agricultura Sinica, 51, 1, pp. 26-36, (2018)
  • [2] BU Yinqiqike, Evaluation methods and influencing factors of cotton color grade and ginning quality, China Fiber Inspection, 4, pp. 57-58, (2019)
  • [3] ZHU Yun, LING Zhigang, ZHANG Yuqiang, Research progress and prospect of machine vision technology, Journal of Graphics, 41, 6, pp. 871-890, (2020)
  • [4] MD MORSHEDUL A, ZHU Zanbo, BERNA E T, Et al., Automatic assessment and prediction of the resilience of utility poles using unmanned aerial vehicles and computer vision techniques, International Journal of Disaster Risk Science, 11, 1, pp. 119-132, (2020)
  • [5] SONG Shuaishuai, HUANG Feng, JIANG Yanbin, Analysis on the research progress of geometric measurement technology based on machine vision, Electronic Measurement Technology, 44, 3, pp. 22-26, (2021)
  • [6] SONG Guoqing, WU Yumin, FENG Yunpeng, Et al., Application of machine vision in optical manufacturing and testing, Imaging Science and Photochemistry, 34, 1, pp. 30-35, (2016)
  • [7] DONG Sheng, YUAN Zhaohui, GU Chao, Et al., Research on intelligent agricultural machinery control platform based on multi-discipline technology integration, Transactions of the CSAE, 33, 8, pp. 1-11, (2017)
  • [8] ALEX J C W, JEFFREY B J., Volcano video data characterized and classified using computer vision and machine learning algorithms, Geoscience Frontiers, 11, 5, pp. 1789-1803, (2020)
  • [9] XIAO Chunyan, HOU Jialin, Building of lint cotton ginning quality grading model, Shandong Agricultural Sciences, 49, 10, pp. 134-138, (2017)
  • [10] YU Hongbin, XIA Bin, Automatic identification of cotton surface impurities based on image processing, Shanghai Textile Science & Technology, 48, 6, pp. 17-19, (2020)
123