基于边缘检测的生产线手机膜缺陷识别方法

目前国内手机保护膜的产量和销量巨大,但手机膜生产线上的缺陷检验仍采用目检法,检测效率与准确率较低.针对生产线上手机膜缺陷检测的4个关键问题(正常与缺陷类别不平衡、高信噪比去噪、边缘特征提取以及缺陷样本检测效率)进行研究.采用RST和图像剪切方法实现缺陷样本扩充,解决缺陷样本少,缺陷位置和形式不足问题;提出自适应小波阈值...

Mobile phone protective film defect recognition method based on edge detection

The production and sales volume of mobile phone protective film in China is huge, but the defect inspection of mobile phone film production line still adopts the visual inspection method, so that the detection efficiency and accuracy are low. Four key problems in the detection of mobile phone protective film defects on the production line are studied: normal and defect category imbalance, high signal-to-noise ratio denoising, edge extra and defect sample detection efficiency. RST and image cutting methods are used to extend defect samples, which can solve the problems of few defective samples, defect positions and forms. The adaptive wavelet threshold and new threshold function are proposed to improve the traditional wavelet threshold denoising method and obtain excellent denoising effect. In the image edge detection technology, the improved wavelet threshold denoising method and the Otsu threshold setting method are introduced to improve the edge detection performance of traditional Canny operators and realize the effective extraction of image features. The Zernike moment with rotation, translation and scale invariance is used to express the features of the edge detection results to improve the efficiency and accuracy of pattern recognition. Finally, the suport vector machine(SVM) is used to identify the Zernike moment features of the edges of normal and defective mobile phone membranes. The experimental result shows that the method has high accuracy and fast detection speed, and meets the defect detection requirements of mobile phone film on the production line.