基于机器视觉的预包装食品检测

基于机器视觉设计了一种缺陷检测系统,该图像处理采用基于偏微分方程的去噪模型实现了图像去噪;利用双阈值分割方法实现了缺陷区域的分割;并采用BP神经网络根据周长、面积和圆形度实现了缺陷分类。结果表明:试验系统的整体漏检率为0.17%,检测精度比较高;每个包装的检测耗时大约为70ms,检测效率比较高;该系统能很好地满足食品包装实时、快速、准确、稳定的检测要求。

Research on pre-packaged food detection based on machine vision

In order to improve the detection accuracy of pre-packaged food, a defect detection system based on machine vision was designed. The detection system mainly includes image acquisition module, image processing and analysis module, output execution module and so on. The image processing method was described in detail. The image denoising model based on partial differential equation was used. The defect region was segmented by double threshold segmentation method. Finally, BP neural network was used to classify defects according to circumference, area and roundness. The feasibility and effectiveness of the method ware verified by experiments. The experimental results show that the overall omission rate is 0.17% and the detection accuracy is relatively high. The detection time of each package is about 70 milliseconds, so the detection efficiency is relatively high. The system can well meet the real-time, rapid, accurate and stable testing requirements of food packaging.