李文采, 李家鹏, 田寒友, 邹昊, 刘飞, 白京, 张振琪, 王辉, 王守伟. 基于RGB颜色空间的冷冻猪肉储藏时间机器视觉判定[J]. 农业工程学报, 2019, 35(3): 294-300. DOI: 10.11975/j.issn.1002-6819.2019.03.037
    引用本文: 李文采, 李家鹏, 田寒友, 邹昊, 刘飞, 白京, 张振琪, 王辉, 王守伟. 基于RGB颜色空间的冷冻猪肉储藏时间机器视觉判定[J]. 农业工程学报, 2019, 35(3): 294-300. DOI: 10.11975/j.issn.1002-6819.2019.03.037
    Li Wencai, Li Jiapeng, Tian Hanyou, Zou Hao, Liu Fei, Bai Jing, Zhang Zhenqi, Wang Hui, Wang Shouwei. Determination of storage time for chilled pork by using RGB color space method based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 294-300. DOI: 10.11975/j.issn.1002-6819.2019.03.037
    Citation: Li Wencai, Li Jiapeng, Tian Hanyou, Zou Hao, Liu Fei, Bai Jing, Zhang Zhenqi, Wang Hui, Wang Shouwei. Determination of storage time for chilled pork by using RGB color space method based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 294-300. DOI: 10.11975/j.issn.1002-6819.2019.03.037

    基于RGB颜色空间的冷冻猪肉储藏时间机器视觉判定

    Determination of storage time for chilled pork by using RGB color space method based on machine vision

    • 摘要: 为解决冷冻猪肉储藏时间在人工判定中准确率和效率低的问题,该研究基于机器视觉和图像处理技术分析了冷冻猪肉表面图像RGB颜色特征,并将无光泽肌肉像素点比例作为自动判定冷冻猪肉储藏时间的特征参数。结果显示:将红度(R)与蓝度(B)的差值小于等于30且红度(R)与绿度(G)的差值小于等于30作为第一特征条件,将红度(R)与蓝度(B)的差值小于等于30、红度(R)与绿度(G)的差值小于等于30且蓝度(B)大于等于100作为第二特征条件对冷冻猪肉颜色特征进行提取时,二值化图像能较好地识别冷冻猪肉表面总肌肉和无光泽肌肉。基于统计学分析,当储藏时间在3个月以内(1、2和3个月)和超过12个月(13、15和17个月)的冷冻猪肉无光泽肌肉像素点比例阈值为26.8%时,冷冻猪肉验证检测判定准确度最佳,分别为90.00% 和 81.67%。经图像分割后提出的基于无光泽肌肉像素点比例的判定方法可以为实际应用提供技术支持和参考。

       

      Abstract: Abstract: The storage time of chilled pork is one of the most important indexes to price of pork. However, storage time of chilled pork was determined by trainees with their own visual senses in most of the Chinese enterprises. This manual grading method demands not only intensive labor but also lacks objectivity and accuracy. The objective of this research was to develop an optimal method for determining the storage time of chilled pork based on computer vision and image processing technologies to meet the requirement of the meat industry. A practical algorithm that can be used in a chilled pork storage time determining system was proposed in this research. The chilled pork sample images were collected by a machine vision image acquisition system. The system consisted of an image acquisition device, computer, and image processing algorithm equipped into the self developed system software. The images of the chilled pork samples in a load table were captured by CCD. Light intensity was regulated through a light controller, and the distance between the camera lens and the chilled pork samples was adjusted though translation stages in the load table. Collected images were automatically stored in the computer for further image processing. First, some methods such as background removal and image denoising were adopted to preprocess the image to obtain a region of interest. In this step, the image was cropped to separate the chilled pork from the background. Then, a color difference method was used to segment the chilled pork area to obtain the area of total muscle and un-glossed muscle. Our results showed that the total and un-glossed muscle of chilled pork can be well distinguished by using binary images when R-B≤30, R-G≤30 (first feature) and R-B≤30, R-G≤30, 100≤B (second feature), respectively. Based on the first and second feature, the number of pixel in the binary images for the total and un-glossed muscle can be obtained from the color information in each pixel, and the ratio of pixel amount between un-glossed muscle and total muscle can also be obtained. We calculated the ratio of un-glossed muscle of different batches, screened the maximum ratio of un-glossed muscle of chilled pork that was stored less than three months and the minimum ratio of un-glossed muscle of chilled pork that was stored more than 12 months, respectively, and then calculated the mean of maximum and minimum value mentioned above. The mean was taken as the threshold value, and that was increased or decreased successively if the actual value was less than or equal to the threshold value mentioned above. The storage time was determined to be within three months if the actual value was greater than the threshold value, and the storage time was determined to be over 12 months. Finally, 60 chilled pork that were stored less than three months and 60 chilled pork that were stored more than 12 months were used to verify the threshold value. Our results showed that, the accuracy of the verification and determination of chilled pork that were stored less than three months (90.00%) and chilled pork that were stored more than 12 months (81.67%) were considerably improved when the threshold value was 26.8%. The results showed that the algorithm can meet the requirements of frozen pork storage time.

       

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