基于机器视觉的玻璃瓶口缺陷检测方法

目的为提高玻璃瓶口缺陷检测精度,确保生产线包装效率。方法基于机器视觉设计一种瓶口缺陷检测方法,并简要介绍检测系统的整体框架。分别论述基于最大熵值法的图像分割方法、瓶口定位方法以及图像特征提取方法,其中图像特征主要包括周长、圆形度、相对圆心距离。利用BP神经网络实现瓶口缺陷的准确识别,将瓶口破损程度转换为具体数值,最后进行实验验证。结果文中检测方法对破损瓶口的检测成功率为99%,对于不同的破损类型均有较高的检测准确度。结论基于机器视觉的玻璃瓶口缺陷检测方法能够满足生产线对准确性和实时性的要求。

Glass-bottle Defect Detection Method Based on Machine Vision

The work aims to improve the glass-bottle defect detection accuracy and ensure production line packaging efficiency. A bottle defect detection method was designed based on machine vision. The overall framework of detection system was briefly introduced. The methods of image segmentation based on maximum entropy, bottle positioning and image feature extraction were respectively discussed. Image features mainly included the perimeter, circularity and relative distance of circle''s center. The accurate bottle defect recognition was realized with BP neural network and the bottle damage degree was converted into a specific value. Finally, the experimental verification was carried out. The success rate of the proposed detection method for the damaged bottle was 99%. It had higher detection accuracy for different damage types. The glass-bottle defect detection method based on machine vision can meet the requirements of production line for accuracy and real-time performance.