人工晶体学报2025，Vol.54Issue(4)：598-604,7.DOI:10.16553/j.cnki.issn1000-985x.2024.0249
基于改进U-Net的BGO晶体粗加工磨砂面弱划痕分割算法
Weak Scratch Segmentation Algorithm for Rough Grinding Surface of BGO Crystal Based on Improved U-Net
摘要
Abstract
BGO crystals usually need to be cut,grind and other rough machining steps to make the grinding surface to improve the performance of components.The extraction and pre-inspection of scratch defects in the rough machining process are very important for the quality evaluation of subsequent crystal components.However,the traditional industrial machine vision algorithm is difficult to finely segment the weak scratches on the rough grinding surface of crystal,which greatly affects the detection efficiency of the subsequent crystal quality.To address the issue of accurately segmenting weak scratches on the crystal grinding surface,this paper adopts an improved U-Net deep learning algorithm.The algorithm embeds a lightweight CBAM attention mechanism into the U-Net architecture to enhance the network's ability to extract shallow scratch features and recover details.Meanwhile,the Copy-paste data augmentation method is employed to improve the generalization of the algorithm model.In addition,in order to alleviate the negative impact of foreground background imbalance in the sample,the loss function adopts Dice Loss and Focal Loss composite multi-loss function.Experimental results show that the proposed algorithm effectively and accurately segments the weak scratches on the rough grinding surface of the crystal,achieving Miou value of 85.2％and accuracy value of 95.4％,which represents an improvement over traditional industrial machine vision algorithms.Furthermore,the algorithm alleviates the issues of false segmentation and under-segmentation of weak scratches to some extent,enabling the pre-detection of scratch defects in the rough machining process,and ultimately reducing unnecessary processes and quality assessment steps in the future,while overall improving the production efficiency of industrial crystal products.关键词
弱划痕提取/BGO晶体/目标分割/U-Net/晶体磨砂面Key words
weak scratch extraction/BGO crystal/target segmentation/U-Net/crystal grinding surface分类
数理科学引用本文复制引用
陶文峰,张晓龙,朱海波..基于改进U-Net的BGO晶体粗加工磨砂面弱划痕分割算法[J].人工晶体学报,2025,54(4):598-604,7.基金项目
国家重点研发计划(2023YFF0715500) （2023YFF0715500）
安徽省重点研究与开发计划(202304a05020009) （202304a05020009）
安徽省科技创新平台重大科技项目(S202305a12020028) （S202305a12020028）
