激光技术2024，Vol.48Issue(3)：443-448,6.DOI:10.7510/jgjs.issn.1001-3806.2024.03.022
基于深度学习的激光散斑图像识别技术研究
Research on laser speckle image recognition technology based on transfer learning
摘要
Abstract
In order to solve the problem that the measurement sensitivity of laser speckles decreases when the water temperature is higher than 20℃,a laser speckle image recognition and detection method based on depth learning is proposed.The speckle image data sets of 20.1℃,20.2℃,and 20.3℃were constructed.A multi-scale convolution neural network was used,combined with appropriate loss function and data enhancement technology,to optimize the characteristics of laser speckle images.Through the training and testing experiments of deep learning models on speckle datasets,high accuracy recognition of underwater temperature information speckle images was achieved,solving the problem of decreased sensitivity in contrast saturation measurement.The experimental results show that compared with AlexNet,VGG,and ResNet models,the accuracy of the GoogleNet model in underwater temperature recognition of speckle images reaches 99%.This study provides theoretical support for the in-depth understanding of temperature field distribution and its impact and provides valuable reference for related application fields.关键词
图像处理/散斑图像/深度学习/GoogleNet/分类识别Key words
image processing/speckle images/deep learning/GoogleNet/classification recognition分类
信息技术与安全科学引用本文复制引用
贺锋涛,吴倩倩,杨祎,张建磊,王炳辉,张依..基于深度学习的激光散斑图像识别技术研究[J].激光技术,2024,48(3):443-448,6.基金项目
XX教育部联合基金资助项目(XXXX) （XXXX）
陕西省技术创新引导专项基金资助项目(2020TG-001) （2020TG-001）
