BOPET薄膜中气泡的检测及识别方法研究

Detection and Identification Methods Research of the Air Bubble in BOPET

双向拉伸聚酯薄膜(BOPET)中的气泡直接影响产品质量,准确快速地检测与识别气泡有重要意义.提出了一种基于LVQ神经网络的BOPET薄膜气泡的检测与识别方法.该算法对采集到的薄膜图像进行处理得到薄膜疵点轮廓,提取长宽比、圆形度、形状复杂性及伸长度4个特征值,输入至已经训练好的基于LVQ神经网络的气泡识别系统中识别气泡并确定其位置与面积.LVQ神经网络的设计的是通过研究BOPET薄膜中气泡的特征,提取特征输入向量,通过训练用的特征值的输入使神经网络达到学习和预测的目的.通过实验测试表明,此方法能满足BOPET薄膜中气泡的检测要求.

Air bubble in biaxially oriented polyester film（BOPET） directly affects the quality of the product, and accurate detection and quick recognition of bubble have important significance. This study presents a bubble in BOPET detection and identification method based on Learning Vector Quantization （LVQ） neural network. In this algorithm, the film images were processed and the outlines of the membrane defects were obtained. Through extrac- ting the aspect ration, circularity, complexity and elongation, the characteristic values of membrane defects, which from the image of film images after image processing, and then input to the bubble recognition system based on LVQ neural network that had been trained. Finally the bubble was distinguished from the other membrane defects, and the recognition system returned the location and area of the bubble in BOPET. Through the study of features of the bubbles in BOPET and extracted some quantities as character input of the LVQ neural network, then input some characteristic values as training value into the LVQ neural network to achieve the learning and prediction purpose, and the The design of the LVQ neural network was designed. The experiments show that, the proposed method can meet the requirements analysis of air bubbles in BOPET film.