欢迎访问《人工晶体学报》官方网站,今天是 2025年6月9日 星期一 分享到:

人工晶体学报 ›› 2025, Vol. 54 ›› Issue (4): 598-604.DOI: 10.16553/j.cnki.issn1000-985x.2024.0249

• 研究论文 • 上一篇    下一篇

基于改进U-Net的BGO晶体粗加工磨砂面弱划痕分割算法

陶文峰1,2, 张晓龙1,2, 朱海波1,2   

  1. 1.合肥知常光电科技有限公司,合肥 230031;
    2.安徽省超光滑表面无损检测重点实验室,合肥 230031
  • 收稿日期:2024-10-21 出版日期:2025-04-15 发布日期:2025-04-28
  • 通信作者: 朱海波,博士。E-mail:hzhu@zc-hightech.com
  • 作者简介:陶文峰(1999—),男,江西省人,工程师。E-mail:wtao@zc-hightech.com
  • 基金资助:
    国家重点研发计划(2023YFF0715500);安徽省重点研究与开发计划(202304a05020009);安徽省科技创新平台重大科技项目(S202305a12020028)

Weak Scratch Segmentation Algorithm for Rough Grinding Surface of BGO Crystal Based on Improved U-Net

TAO Wenfeng1,2, ZHANG Xiaolong1,2, ZHU Haibo1,2   

  1. 1. Hefei ZC Optoelectronic Technologies Co., Ltd., Hefei 230031, China;
    2. Key Laboratory of Non-Destructive Detection for Ultra-Smooth Surface in Anhui Province, Hefei 230031, China
  • Received:2024-10-21 Online:2025-04-15 Published:2025-04-28

摘要: BGO晶体通常需要通过切割、磨削等粗加工步骤制作磨砂面以提升元件性能,其粗加工过程中划痕缺陷的提取和预检对后续晶体元件的质量评估极为重要。然而,传统工业机器视觉算法难以精细分割晶体粗加工磨砂面上的弱划痕,极大地影响了后续晶体品质的检测效率。针对磨砂面弱划痕难以精确分割的难题,本文采用了一种改进的U-Net 深度学习算法,该算法在U-Net基础结构中嵌入了轻量级CBAM注意力机制,提升网络对浅划痕特征提取和细节恢复能力;同时采用Copy-paste数据增广方法,提升算法模型的泛化性;另外为了缓解样本中前景背景不平衡带来的消极影响,采用Dice Loss、Focal Loss复合损失函数。实验表明,本文算法对于晶体粗加工磨砂面的弱划痕能有效进行精确分割,并且其平均交并比Miou为85.2%,准确率为95.4%,相较于传统工业机器视觉算法有明显提升。此外该算法一定程度上解决了弱划痕的误分割与欠分割现象,能对粗加工过程中的晶体进行划痕缺陷预先检测,减少后续不必要的工艺和质量评估步骤,同时整体提高工业晶体产品的生产效率。

关键词: 弱划痕提取, BGO晶体, 目标分割, U-Net, 晶体磨砂面

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.

Key words: weak scratch extraction, BGO crystal, target segmentation, U-Net, crystal grinding surface

中图分类号: