结合视觉显著性和卷积神经网络的提花织物疵点检测技术

Jacquard Fabric Defect Detection Technology Combining Context-awareness and Convolutional Neural Network

为了实现提花织物疵点自动检测,提出了一种结合视觉显著性和卷积神经网络的提花织物疵点检测方法。针对提花织物背景干扰的问题,利用视觉显著性模型(Context-aware,CA)抑制背景信息,突出疵点区域的显著性来获得图像的显著图;为了区分织物图像中是否存在疵点,使用在通用数据集上训练过的VGG16神经网络模型对提花织物图像的显著图分类。结果表明:该方法在提花织物疵点检测上平均准确率为97.07%,比直接利用VGG16模型对提花织物疵点检测的准确率提高了19.44%,是一种适合提花织物疵点检测的方法。

In order to achieve the automatic detection of jacquard fabric defects,a method is proposed to detect jacquard fabric defects,which combines context-awareness and convolutional neural network.In order to solve the problem of background interference in the jacquard fabric,a context-aware(CA)model was used to suppress the background information and highlight the salience of the defect area to obtain a context-aware view of the image.To distinguish whether there are defects in the fabric image,the VGG16 neural network model trained on the general data set was used to classify the context-aware views of the image.The results show that this method has an average accuracy of 97.07%in the detection of jacquard fabric defects,which is 19.44%higher than that of the detection of jacquard fabric defects by the direct use of the VGG16 model.It is a suitable method for detecting jacquard fabric defects.