基于机器视觉和卷积神经网络的不溶异物检测

Detection insoluble foreign matter based on machine vision and convolutional neural network

药液在生产过程中易混入不溶异物,因此投入市场前的必须对药液进行检测。针对安瓿瓶药液检测,区别于传统的序列图像检测算法,设计了一种基于机器视觉和卷积神经网络的检测方法。首先利用Canny边缘检测提取安瓿瓶瓶壁边缘,裁剪药液区域图像,减少了后续计算量;其次改用VGG16卷积神经网络进行不溶异物的特征提取,可以提取到传统特征之外的抽象特征;最后通过迁移学习和微调,在400张测试样本中,结果为识别正确378张。结果表明,该方法可以检出不溶异物,满足实际生产需求。

Liquid medicine is easy to mix with insoluble foreign matter during the production process. Therefore, the liquid medicine must be tested in time before being put on the market. A detection method based on machine vision and convolutional neural network is designed for ampoule liquid detection, which is different from the traditional sequential image detection algorithm. First, Canny edge detection is used to extract the edge of the ampoule bottle, and the image of the liquid area is cropped, reduced the amount of subsequent calculations. Secondly, the VGG16 convolutional neural network is used for feature extraction of insoluble foreign bodies, which can extract abstract features other than traditional features. Finally, through transfer learning and fine-tuning, 378 of the 400 test samples were identified as correct.The results show that this method can detect insoluble foreign bodies and meet the actual production requirements.