Author Affiliations
Abstract
1 College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2 State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Collaborative Innovation Center of Quantum Matter, Tsinghua University, Beijing 100084, China
Single-pixel imaging can reconstruct the image of the object when the light traveling from the object to the detector is scattered or distorted. Most single-pixel imaging methods only obtain distribution of transmittance or reflectivity of the object. Some methods can obtain extra information, such as color and polarization information. However, there is no method that can get the vibration information when the object is vibrating during the measurement. Vibration information is very important, because unexpected vibration often means the occurrence of abnormal conditions. In this Letter, we introduce a method to obtain vibration information with the frequency modulation single-pixel imaging method. This method uses a light source with a special pattern to illuminate the object and analyzes the frequency of the total light intensity signal transmitted or reflected by the object. Compared to other single-pixel imaging methods, frequency modulation single-pixel imaging can obtain vibration information and maintain high signal-to-noise ratio and has potential on finding out hidden facilities under construction or instruments in work.
single-pixel imaging frequency modulation vibration measurement 
Chinese Optics Letters
2023, 21(1): 011102
作者单位
摘要
1 天津工业大学机械工程学院, 天津 300387
2 天津市现代机电装备技术重点实验室, 天津 300387
3 北京大恒图像视觉有限公司, 北京 100085
采用深度学习方法对棉花中的异性纤维进行分类识别。首先建立异性纤维数据集,针对异性纤维尺寸和形状多样性的特点,采用基于Faster RCNN的目标识别框架,以RseNet-50代替原始的VGG16作为异性纤维分类模型的特征提取网络,并采用k-means++聚类算法对候选框生成尺寸进行改进;然后对模型进行训练,实现棉花中异性纤维的分类和定位。训练后的模型在验证集上的准确率达到94.24%,精度为98.16%,召回率为95.93%,精确率和召回率的调和平均数(F1分数)为0.970。对比改进前、后模型对异性纤维的识别效果,改进后的模型在小尺寸、大长宽比和密集出现的情况具有更好的识别效果,相对于原始模型,其准确率、精度、召回率和F1分数分别提高了3.21%、0.90%、2.51%和0.017。
图像处理 异性纤维 深度学习 目标识别 Faster RCNN k-means++ 
激光与光电子学进展
2020, 57(12): 121007

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