Journals >Optics and Precision Engineering
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2024
Volume: 32 Issue 2
13 Article(s)
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[in Chinese]
Design and optimization of cryogenic installation structure for gratings of Long-wave Infrared Spatial Heterodyne Interferometer
Yang WU, Yutao FENG, Bin HAN, Junqiang WU, and Jian SUN
The Long-wave Infrared Spatial Heterodyne Interferometer may have interference fringe distortion due to non-uniform stress acting on the optical components under cryogenic conditions， which will cause performance degradation of the interferometer system. To solve the problem of interference fringe distortion under cryoThe Long-wave Infrared Spatial Heterodyne Interferometer may have interference fringe distortion due to non-uniform stress acting on the optical components under cryogenic conditions， which will cause performance degradation of the interferometer system. To solve the problem of interference fringe distortion under cryogenic conditions， this paper analyzed the factors affecting interference fringe distortion based on the initial optical mechanical system of Long-wave infrared spatial heterodyne interferometer， and combined the optical-mechanical-thermal coupling analysis method to simulate the cryogenic state of the interferometer system. Then， a cryogenic micro-stress dynamic stable installation structure was designed for grating， which is the key component affecting fringe distortion. After the optimization of structure， the Root-Mean-Square（RMS） and Peak-to-Valley（PV） values of grating’s surface shape are 3.89×10-2 nm and 2.21×10-1 nm， respectively， which are five orders of magnitude lower than the initial structure analysis results. The simulated interference fringe distortion is less than 1 detector pixel. The cryogenic verification test of whole system shows that the optimized structure can effectively reduce the distortion of interference fringe， and the distortion is less than 2 detector pixels. The experimental results are highly consistent with the simulation results， which verifies the effectiveness of the optimization analysis method. The optimization analysis method has great significance and value for improving the structural stability and operating performance of the cryogenic reflective optical system..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 171 (2024)
Self-correction for resolver second harmonic angle measurement error
Yingguang WANG, Jiyang ZHANG, Qiang ZHANG, Ming LU, and Limei TIAN
The resolver amplitude error and quadrature error are expressed in the angular velocity spectrum as the second harmonic of the rotational frequency， which is the main source of the resolver angle measurement error and affects the angular velocity control accuracy and stability of the servo system. In this paper， a selfThe resolver amplitude error and quadrature error are expressed in the angular velocity spectrum as the second harmonic of the rotational frequency， which is the main source of the resolver angle measurement error and affects the angular velocity control accuracy and stability of the servo system. In this paper， a self-correction method for the second harmonic error of resolver based on characteristic frequency reference was proposed. Firstly， the mechanism of the resolver error was analyzed， and the mutual irrelevance of the amplitude error and quadrature error was obtained， and it was proved that the second harmonic error correction could be realized by adjusting the amplitude and phase difference of the resolver output signal. Then， the amplitude corrector based on proportional amplification and the phase angle corrector based on cross-adjustment were designed between the resolver and the Resolver-to-Digital Converter （RDC）. Finally， according to the constant characteristic frequency of the error signal in the linear control system， the servo system was controlled at a constant speed， and the amplitude of the second harmonic frequency in the angular velocity spectrum was used as the reference to adjust the amplitude corrector and phase corrector respectively， to correct second harmonic errors. The experimental results show that the method can reduce the second harmonic angle measurement error of the resolver by 78.5%， and the speed fluctuation of the servo system can be reduced by 40.5%.The method realizes the self-correction of the second harmonic error of the resolver， and can greatly improve the measurement accuracy of the resolver and the rotation stability of the servo system..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 184 (2024)
Semiparametric dynamic model identification for hyper-redundant manipulator based on iterative optimization and neural network compensation
Yufei ZHOU, Zhongcan LI, Yi LI, Jingkai CUI... and Mingchao ZHU|Show fewer author(s)
In order to achieve accurate dynamic model identification of the hyper-redundant manipulator， a semiparametric dynamic model identification method based on iterative optimization and neural network compensation was proposed. First， the dynamic model of the hyper-redundant manipulator and the base parameter set were intIn order to achieve accurate dynamic model identification of the hyper-redundant manipulator， a semiparametric dynamic model identification method based on iterative optimization and neural network compensation was proposed. First， the dynamic model of the hyper-redundant manipulator and the base parameter set were introduced， joint nonlinear friction model was established， and the excitation trajectory was generated using genetic algorithm to optimize the condition number of the regression matrix. Second， the physical feasibility constraint of the manipulator dynamic model was established， and a two loops identification network was designed to identify the inertial parameters and joint friction model of the hyper-redundant manipulator based on the iterative optimization method. Finally， the BP neural networks were trained to obtain the semiparametric dynamic model of the hyper-redundant manipulator by using data set. A series of identification algorithms were compared and analyzed. The experimental results show that， compared with the traditional least squares algorithm and weighted least squares algorithm， the identification algorithm proposed in this paper can improve the sum of identify torque residual root mean square （RMS） of joints by 32.81% and 23.76%， respectively. The sum of torque residuals of the semi-parametric dynamic model is 23.56% higher than that of the full-parametric dynamic model. The identification results verify the effectiveness of the proposed identification method..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 193 (2024)
[in Chinese]
Study of a consistent assembly system for AFM probes based on beam deflection method
Baoliang ZHANG, Wenfeng LIANG, Tie YANG, and Peng YU
To solve the problem of complex and time-consuming optical path adjustment after probe replacement in Atomic Force Microscope(AFM) systems， this paper presents the first proposal to achieve the consistency of the replaced probe with respect to the original optical path position of the AFM system by precisely controllinTo solve the problem of complex and time-consuming optical path adjustment after probe replacement in Atomic Force Microscope(AFM) systems， this paper presents the first proposal to achieve the consistency of the replaced probe with respect to the original optical path position of the AFM system by precisely controlling the probe and probe holder assembly position， thus eliminating the need to adjust the optical path after needle replacement in AFM systems. The optical path consistency component of the system used the beam deflection method to amplify and monitor the probe position and deflection， and used a high-precision displacement and angle adjustment platform to adjust the orientation of the probe relative to the probe clip. The probe consistency effect was verified by physical construction， and the impact of probe position deflection due to Ultraviolet （UV） glue curing process； the impact of detector noise on the imaging quality of the AFM system when the probe is deflected by different amounts was systematically analyzed. The experimental results show that the average position accuracy of the probes assembled by the system is close to 1.1 µm， and it takes only 8 seconds to change the consistent probes in the AFM system. The system achieves high precision and consistent probe assembly， which greatly simplifies the operation of the AFM system to recalibrate the optical path， and can effectively improve the operation and measurement performance of industrial metrology AFM when combined with the automatic needle changing device..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 137 (2024)
Design of wide spectrum multi-sensor axis consistency detection system
Yun ZOU, Yundong ZHU, and Jinsong WANG
Optical axis consistency is an important indicator for measuring the performance of multi-sensor optoelectronic systems. In order to address the issue of narrow working spectral range and limited system flexibility in the multi-sensor axis consistency detection system，this paper designed a board-spectrum multi-sensor aOptical axis consistency is an important indicator for measuring the performance of multi-sensor optoelectronic systems. In order to address the issue of narrow working spectral range and limited system flexibility in the multi-sensor axis consistency detection system，this paper designed a board-spectrum multi-sensor axis consistency detection system. The system based on the concepts of optical path switching and opto-thermal conversion. The system used a Cassegrain reflective optical system as the receiving and transmitting system in the range from visible light to long-wave infrared. A stepper motor was used to move the reflective mirror above the guide rail， allowing for the switching of the system's optical path. The copper sulfide-coated germanium glass was used to convert the short-wavelength spot into a hot spot as the photothermal conversion target. The long-wave infrared detector was used to collect the laser spot images of various spectral bands. The system could detect the spectral range of 0.4 - 14 μm. The analysis of the optical system's image quality shows that the root mean square （RMS） diameter caused by aberrations is consistently below 9 μm across various wavelengths. The system also exhibits strong energy concentration. Detection accuracy of the system is analyzed， and the maximum measurement error is 0.1 mrad. Experiments on repetitive precision involving the guide rail's back-and-forth movement， and accuracy measurement experiments on the system， verify the reliability of the system. The results indicate that the detection system meets the requirement of instrument accuracy level 1.5. The detection system has a compact structure， a wide range of applicable spectral， and the ability to perform axis consistency testing for multi-sensor optoelectronic devices..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 148 (2024)
Geometric errors vision inspection and error evaluation method of rudder structural parts
Zeqing YANG, Enxu PING, Yingshu CHEN, Ning HU... and Yali LÜ|Show fewer author(s)
In view of the problems that existing contact measurement methods can only measure one or two parameters after clamping and positioning， and low inspection efficiency， and combining the machine vision inspection technology has outstanding advantages such as no contact， no damage， high degree of automation and safety anIn view of the problems that existing contact measurement methods can only measure one or two parameters after clamping and positioning， and low inspection efficiency， and combining the machine vision inspection technology has outstanding advantages such as no contact， no damage， high degree of automation and safety and reliability， a geometric errors vision inspection method of the air rudder was proposed. Firstly， the images of rudder shaft and rudder surface area were acquired by a computer vision system， and image distortion was removed using the camera parameters and distortion parameters obtained from the camera calibration. Image pre-processing techniques were used to remove noise and reduce the impact of non-target elements in the detection environment on the detected object. In order to extract the rudder-like structural member edges more efficiently， Sobel operator， Scharr operator， Laplace operator and Canny operator were used to detect the edges of the image to determine the contours.In order to more effectively extract the edge and determine the contours of the air rudder， the Sobel operator， Scharr operator， Laplace operator， and Canny operator were used to detect the edge detection of images. The experimental results show that the edges of the rudder shaft image were sharper and more complete after the Scharr operator， and the edges of the rocker image were sharper after the Canny operator. Therefore， the Scharr operator was used to extract the edges of the rudder image and the Canny operator to extract the edges of the rocker image. Combining the characteristics of the testing elements， Hoff straight line and Hoff circle detection methods are used to extract rudder surface edge line features， rudder shaft bus features， and rocker arm circle contour features. And the reference elements of the rudder core symmetry， rudder shaft perpendicularity， and rocker arm pinch angle are determined. Construct an objective function for geometric quantity error detection and compute the optimal solution using an adaptive genetic algorithm. The measured values of rudder core symmetry， rudder axis perpendicularity and rocker angle are obtained from the internal and external reference matrix obtained from the camera calibration. Finally， the vision inspection software for geometric and assembly errors of air rudder parts has been developed， and the vision inspection experiment platform has been constructed to realize the rapid detection of geometric and assembly angle errors of air rudder parts. After several repetitive measurement experiments， the symmetry inspection accuracy reaches 0.086 mm， the perpendicularity inspection accuracy reaches 0.233 mm and the assembly angle inspection accuracy reaches 0.373°， and the detection time is 7 s.The experimental results show that the method not only improves the accuracy and efficiency of geometric errors detection， but also helps to improve the automation and intelligence of forming-manufacturing-in-machine inspection of air rudder parts..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 158 (2024)
[in Chinese]
Phase unwrapping technology about point diffraction interference fringe based on atrous spatial convolutional networks
Tongmeng WANG, Fen GAO, and Bing LI
In order to meet the high-precision， high-efficiency， and anti-interference detection requirements of point diffraction interference measurement for phase unwrapping algorithms. Atrous Spatial Convolutional Networks -based phase unwrapping method for phase-shifted point diffraction interference images was proposed. By In order to meet the high-precision， high-efficiency， and anti-interference detection requirements of point diffraction interference measurement for phase unwrapping algorithms. Atrous Spatial Convolutional Networks -based phase unwrapping method for phase-shifted point diffraction interference images was proposed. By combining the autoencoder structure and the adaptive spatial convolution， higher phase unwrapping accuracy was achieved， and the degradation of the network model was effectively prevented， realizing controllable multi-scale feature extraction of the wrapped phase image. A large and diverse dataset of point diffraction phase data was used for training and optimization， which accurately identifies the order of the wrapped phase and quickly processed the wrapped image to obtain high-precision unwrapping results. The proposed method was applied to actual point diffraction interference images and compared with results from ESDI professional interference image processing software and other unwrapping algorithms. The results show that the unwrapping results have an RMSE value of 0.022 2 rad compared to the software processing results， with a surface fitting result PV difference of only 0.012 1λ and an RMS difference of only 0.004 2λ. In terms of time efficiency， it takes only 0.035 s on average to complete the processing of an image， while the traditional methods are all greater than 1 s. Compared to other methods， the proposed method exhibits fast and high-precision characteristics in unwrapping wrapped phase， providing a new feasible solution for high-precision phase unwrapping in point diffraction interference image processing..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 208 (2024)
PET/CT Cross-modal medical image fusion of lung tumors based on DCIF-GAN
Tao ZHOU, Qianru CHENG, Xiangxiang ZHANG, Qi LI, and Huiling LU
Medical image fusion based on Generative Adversarial Network （GAN） is one of the research hotspots in the field of computer-aided diagnosis. However， the problems of GAN-based image fusion methods such as unstable training， insufficient ability to extract local and global contextual semantic information of the images， Medical image fusion based on Generative Adversarial Network （GAN） is one of the research hotspots in the field of computer-aided diagnosis. However， the problems of GAN-based image fusion methods such as unstable training， insufficient ability to extract local and global contextual semantic information of the images， and insufficient interactive fusion. To solve these problems， this paper proposed a dual-coupled interactive fusion GAN （DCIF-GAN）. Firstly， a dual generator and dual discriminator GAN was designed， the coupling between generators and the coupling between discriminators was realized through the weight sharing mechanism， and the interactive fusion was realized through the global self-attention mechanism； secondly， coupled CNN-Transformer feature extraction module and feature reconstruction module were designed， which improved the ability to extract local and global feature information inside the same modal image； thirdly， a cross modal interactive fusion module （CMIFM） was designed， which interactively fuse image feature information of different modalities. In order to verify the effectiveness of the proposed model， the experiment was carried out on the lung tumor PET/CT medical image dataset. Compared with the best method of the other four methods， the proposed method in the average gradient， spatial frequency， structural similarity， standard deviation， peak signal-to-noise ratio， and information entropy are improved by 1.38%， 0.39%， 29.05%， 30.23%， 0.18%， 4.63% respectively. The model can highlight the information of the lesion areas， and the fused image has clear structure and rich texture details..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 221 (2024)
Audio object detection network with multimodal cross level feature knowledge transfer
Shibei LIU, and Ying CHEN
As one of the inherent properties of objects， sound can provide valuable information for target detection. At present， the method of target positioning only by monitoring environmental sound is less robust. To solve this problem， a multi-modal self-supervised target detection network under cross-level feature knowledgeAs one of the inherent properties of objects， sound can provide valuable information for target detection. At present， the method of target positioning only by monitoring environmental sound is less robust. To solve this problem， a multi-modal self-supervised target detection network under cross-level feature knowledge transfer was proposed. First of all， in view of the teachers network and students at the same characteristics of network learning ability of the limited problem， design based on the integration of teachers across level knowledge transfer loss， through the way of attention fusion deep and shallow characteristics of students， more efficient learning to the corresponding teacher middle layer characteristics， to extract more knowledge， combined with KL divergence， realize the alignment of teachers and students network alignment. In addition， in order to solve the problem of missing localization information， localization distillation loss was added， and more localization information was obtained by fitting the distribution of the teacher. With the network trained in the multimodal audiovisual detection MAVD dataset， the mAP values improve by 6.71%， 14.36% and 10.32% from the baseline network at IOU values of 0.5，0.75 and average， respectively. The experimental results demonstrate the superiority of this detection network..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 237 (2024)
Multimodal medical image fusion method based on structural functional cross neural network
Jing DI, Wenqing GUO, Li REN, Yan YANG, and Jing LIAN
To solve the problems of texture detail blurring and low contrast in multimodal medical image fusion， a multimodal medical image fusion method with structural-functional crossed neural networks was proposed. Firstly， this method designed a structural and functional cross neural network model based on the structural andTo solve the problems of texture detail blurring and low contrast in multimodal medical image fusion， a multimodal medical image fusion method with structural-functional crossed neural networks was proposed. Firstly， this method designed a structural and functional cross neural network model based on the structural and functional information of medical images. Within each structural-functional cross module， a residual network model was also incorporated. This approach not only effectively extracted the structural and functional information from anatomical and physiological medical images but also facilitated interaction between structural and functional information. As a result， it effectively captured texture details from multi-source medical images， creating fused images that closely align with human visual characteristics. Secondly， a new attention mechanism module was constructed by utilizing the effective channel attention mechanism and spatial attention mechanism model （ECA-S）， which continuously adjusted the weights of structural and functional information to fuse images， thereby improving the contrast and contour information of the fused image， and to make the fused image color more natural and realistic. Finally， a decomposition process from the fused image to the source image was designed， and since the quality of the decomposed image depends directly on the fusion result， the decomposition process could make the fused image contain more texture detail information and contour information of the source image. By comparing with seven high-level methods for medical image fusion proposed in recent years， the objective evaluation indexes of AG， EN， SF， MI， QAB/F and CC of this paper's method are improved by 22.87%， 19.64%， 23.02%， 12.70%， 6.79% and 30.35% on average， respectively， indicating that this paper's method can obtain fusion results with clearer texture details， higher contrast and better contours in subjective visual and objective indexes are better than other seven high-level contrast methods..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 252 (2024)
Design of lightweight re-parameterized remote sensing image super-resolution network
Jianbing YI, Junkuan CHEN, Feng CAO, Jun LI, and Weijia XIE
In response to the high hardware requirements associated with the deployment of current deep learning-based remote sensing image super-resolution reconstruction models， this paper presented a lightweight， re-parameterized residual feature remote sensing image super-resolution reconstruction network. Firstly， a residualIn response to the high hardware requirements associated with the deployment of current deep learning-based remote sensing image super-resolution reconstruction models， this paper presented a lightweight， re-parameterized residual feature remote sensing image super-resolution reconstruction network. Firstly， a residual local feature module was designed using re-parameterization to effectively extract local image features. Simultaneously considering the occurrence of similar features within images， a lightweight global context module was devised to associate similar features in images， enhancing the network's feature representation capability. The channel compression rate of this module was adjusted to reduce the model's parameter count and improve its performance. Finally， a multi-level feature fusion module was employed before the upsampling module to aggregate deep features and generate a more comprehensive feature representation. Tested on the UC Merced remote sensing dataset， this algorithm exhibits a parameter count of 539 K for ×3 super-resolution， a PSNR of 30.01 dB， a SSIM of 0.844 9， and an inference time of 0.010 s. In comparison， the HSENet algorithm has a parameter count of 5 470 K， a PSNR of 30.00 dB， an SSIM of 0.842 0， and an inference time of 0.059 s. Experimental results demonstrate that this algorithm outperforms the HSENet algorithm， featuring fewer parameters， faster execution， and notable improvements in PSNR and SSIM. Testing on the DIV2K natural image dataset reveals that this algorithm exhibits advantages in PSNR and SSIM compared to other algorithms， demonstrating its strong generalization capability..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 268 (2024)
Concrete crack segmentation combined with linear guidance and mesh optimization
Guanghui LIU, Jian CHEN, Yuebo MENG, and Shengjun XU
A model was proposed to address issues with low segmentation accuracy， leakage of tiny cracks， and background interference in the segmentation process of concrete surface cracks. The model combined linear guidance and mesh optimization for crack segmentation. Firstly， the backbone network was enriched with a multi-branA model was proposed to address issues with low segmentation accuracy， leakage of tiny cracks， and background interference in the segmentation process of concrete surface cracks. The model combined linear guidance and mesh optimization for crack segmentation. Firstly， the backbone network was enriched with a multi-branch linear guidance module. The network's ability to represent the linear structure of cracks was boosted by adaptive single-dimensional pooling. This facilitated the establishment of connections between cracks in different areas， enhanced the capability to perceive global context data， and improved the network's segmentation accuracy. Then， a module for mesh detail optimization is proposed， which divides the entire spatial domain into several spatial meshes through the three steps of partitioning， optimization， and merging. The fine cracks' information in the spatial meshes was extracted to prevent the leakage of fine cracks. Finally， a mixed attention module was embedded in the skip connections of the backbone network， highlighting crack features in the two-dimensional space and channels while also reducing background interference. On the Deepcrack537， Crack500， and CFD crack datasets， the proposed model achieves IoU values of 77.07%， 58.96%， and 56.55%， respectively. The F1-score values also performs well， achieving 87.05%， 74.19%， and 72.24%， respectively. These results are significantly better than those of most existing methods， with superior segmentation accuracy..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 286 (2024)
Flue-cured tobacco leaf grade detection through multi-receptive field features fusing adaptively and dynamic loss adjustment
Zifen HE, Yang LUO, Yinhui ZHANG, Guangchen CHEN... and Lin XU|Show fewer author(s)
Rapid and accurate detection of flue-cured tobacco leaf grade is integral to the advancement of tobacco intelligent equipment，promoting refined management of agricultural products. Aiming at the issue that it is difficult to distinguish flue-cured tobacco leaves with high similarity between different grades， a flue-curRapid and accurate detection of flue-cured tobacco leaf grade is integral to the advancement of tobacco intelligent equipment，promoting refined management of agricultural products. Aiming at the issue that it is difficult to distinguish flue-cured tobacco leaves with high similarity between different grades， a flue-cured tobacco leaf grade detection network （FTGDNet） through multi-receptive field feature fusing adaptively and dynamic loss adjustment was proposed. Firstly， FTGDNet adopted CSPNet and GhostNet as feature extraction backbone network and auxiliary feature extraction network to enhance the model feature extraction ability， respectively；Secondly，to merge global feature information and local detail feature information，an explicit visual center bottleneck module （EVCB） was embedded at the end of backbone network； Moreover， a multi-receptive field feature adaptive fusion module （MRFA_d） was constructed， in which the attention feature fusion （AFF） mechanism adaptively fuses the weights of feature maps with different receptive fields to highlight the effective channel information while enhancing the local receptive fields of the model； In addressing the decrease of positioning accuracy due to CIoU_Loss performance degradation when the prediction box and real box shared the same aspect ratio and their centers align during the regression positioning process， a new positioning loss function MCIoU_Loss was designed， In addition， the rectangular similarity attenuation coefficient was introduced to dynamically adjust the similarity discriminant of prediction box and real box to accelerate the model fitting. The experimental results show that the verification accuracy and test accuracy of FTGDNet for 10 grades of flue-cured tobacco leaf reached 90.0% and 87.4%， respectively， with an inference time of 12.6 ms. Compared with various advanced object detection network， FTGDNet achieves higher detection accuracy and faster detection speed， which could provide technical support for high-precision flue-cured tobacco leaf grade detection..
Optics and Precision Engineering
- Publication Date: Jan. 25, 2024
- Vol. 32, Issue 2, 301 (2024)
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