现代电子技术2024，Vol.47Issue(23)：55-61,7.DOI:10.16652/j.issn.1004-373x.2024.23.009
基于深度学习的结晶过程原位图像分割方法
Deep learning based in-situ image segmentation method for crystallization process
摘要
Abstract
A crystal in-situ image segmentation method based on improved YOLOv8 is proposed to address the crystal segmentation difficulties caused by low pixels,overlapped crystals and background interference of the in-situ images in the process of crystallization.To improve the segmentation detection performance of the model,the efficient multi-scale attention(EMA)mechanism is introduced to enhance the perception ability of the model.Subsequently,the original convolutional block is improved by the space-to-depth non-strided convolution(SPD-Conv)method,so as to enhance the segmentation accuracy of crystals(the objects)with low pixel and small size while reducing the computational effort of the model.Finally,the efficient intersection over union(EIoU)loss function is used to optimize the detection results of the occluded and overlapped crystals(the objects).The experimental results show that the crystal detection accuracy(mAP)of the proposed algorithm reaches 71.3%,its accuracy is improved by 5.3%,and its floating-point calculation burden is reduced by 1.9 GFLOPs.In addition,the proposed method has advantages in improving the quality of crystal image and eliminating the crystal overlap.关键词
原位图像/晶体/图像分割/YOLOv8/注意力机制/损失函数Key words
in-situ image/crystal/image segmentation/YOLOv8/attention mechanism/loss function分类
电子信息工程引用本文复制引用
褚腾飞,孙科,张方坤,单宝明,徐啟蕾..基于深度学习的结晶过程原位图像分割方法[J].现代电子技术,2024,47(23):55-61,7.基金项目
国家自然科学基金资助项目(62103216) （62103216）
山东省自然科学基金资助项目(ZR2020QF060) （ZR2020QF060）
