国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

With the development of science and technology and the popularity of electronic equipment, glass screen quality has become an important consideration for electronic equipment and other products; The detection of glass appearance defects is the most important link in glass quality detection, which is also the key link to ensure the production of high quality and high-performance glass products; At present, there are some problems in the detection methods of glass surface defects, such as resource consumption of target-free training image, low detection accuracy and difficult extraction of complex feature information. Therefore, to solve the above problems, a defect segmentation model of mobile phone glass screens based on U-pyramid pooling module-Net(U-P-Net) is proposed. Superpixel preprocessing is used to reduce the complexity of the original image effectively. ResNet50 was used as a classification network to reduce the waste of resources caused by training images without targets and improve training efficiency. U-P-Net is proposed, which aggregates the context information of different regions effectively and improves the ability to obtain global information. Experimental results show that the proposed U-P-Net glass defect segmentation algorithm is significantly superior to other traditional convolutional neural network segmentation methods, which proves the effectiveness of the framework on mobile screen data sets.