Journals >Optics and Precision Engineering
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2024
Volume: 32 Issue 23
12 Article(s)
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All-fiber passive Q-switched laser based on stimulated Brillouin scattering
Guanrui ZHAO, Tingwu GE, Qingxuan LI, Wenqi ZHANG... and Guanzheng LI|Show fewer author(s)
This paper presents an all-fiber， passively Q-switched pulsed laser system with enhanced laser damage threshold and high-power output. A 1060-XP single-mode fiber serves as the scattering medium， utilizing Rayleigh scattering （RS） and stimulated Brillouin scattering （SBS） effects. A fiber Bragg grating with a central wThis paper presents an all-fiber， passively Q-switched pulsed laser system with enhanced laser damage threshold and high-power output. A 1060-XP single-mode fiber serves as the scattering medium， utilizing Rayleigh scattering （RS） and stimulated Brillouin scattering （SBS） effects. A fiber Bragg grating with a central wavelength of 1 064 nm and 10% reflectivity forms the resonant cavity mirror. The system produces a bidirectional， tunable laser output with stable Q-switching characteristics under varying pump power. The transition of the laser output through Amplified Spontaneous Emission （ASE）， random lasing， and Q-switched pulsed operation is analyzed. At a pump power of 48.15 W， the system achieves a near-infrared supercontinuum output with a 7-ns pulse width， single-end output power of 11.79 W， wavelength coverage of 1 030-1 650 nm， 23.2% slope efficiency， and a peak power of up to 17 kW. The repetition rate is tunable from 3.7 to 100 kHz. This compact， robust laser design eliminates fragile components， offering a fully integrated， miniaturized solution for pulsed fiber lasers..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3409 (2024)
Self-reference focusing imaging for common-path shearing digital holographic microscopy
Linlin HUANG, Bingcai LIU, Hongjun WANG, Xueling ZHANG... and Ailing TIAN|Show fewer author(s)
This paper presents a self-reference focusing digital holographic microscopy method based on common-path shearing. The method employs a dual-polarization grating shearing device that utilizes the polarization grating’s spectral properties to replicate the object wave carrying sample information. By adjusting the distanThis paper presents a self-reference focusing digital holographic microscopy method based on common-path shearing. The method employs a dual-polarization grating shearing device that utilizes the polarization grating’s spectral properties to replicate the object wave carrying sample information. By adjusting the distance between the dual-polarization gratings， the spacing between the micrograph and the holographic interference image on the camera can be flexibly controlled， enabling simultaneous recording of both. Through recording multiple images from defocus to focus to defocus， the Brenner gradient method is applied to determine the optimal focus， achieving self-reference focusing. The ASM is then used to reconstruct the interference region’s phase at the best focus. Experimental verification， using a phase resolution plate， demonstrated a measured phase height of 0.336 μm with an average absolute error of 0.023 μm. The proposed method features high accuracy， stability， and a compact design..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3416 (2024)
Sensitivity improvement of NO gas detection based on mid-infrared TDLAS
Wenjin XING, Runhao LI, and Yunfeng BI
Accurate detection of nitric oxide （NO） is crucial for applications in environmental， biomedical， and other fields. NO demonstrates strong absorption in the mid-infrared wavelength range， enabling high-sensitivity detection using tunable diode laser absorption spectroscopy （TDLAS） with a mid-infrared quantum cascade laAccurate detection of nitric oxide （NO） is crucial for applications in environmental， biomedical， and other fields. NO demonstrates strong absorption in the mid-infrared wavelength range， enabling high-sensitivity detection using tunable diode laser absorption spectroscopy （TDLAS） with a mid-infrared quantum cascade laser as the light source. This study develops a three-stage thermoelectric cooler （TEC）-based temperature control structure， introduces a concentration inversion model， and enhances detection sensitivity through optimized laser temperature stability and gas concentration inversion. Experimental results reveal rapid stabilization of laser temperature at room temperature with a stability of 0.003 ℃. The proposed model outperforms the 2f/1f model under identical conditions. Utilizing the signal's peak-to-peak value for concentration inversion over a 30 m optical path and with three-stage laser temperature control， the system achieves an NO concentration measurement linearity of 0.997 5 and a detection limit of 37.5×10-9 within a 0-500×10-6 range. By optimizing both laser temperature control and the inversion model， the NO detection sensitivity is improved by two orders of magnitude..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3424 (2024)
Stars removal of all day time mid-high orbit target detection system
Liduo SONG, Kainan YAO, Zhiqiang XU, Jianli WANG... and Haoran JIA|Show fewer author(s)
In ground-based optical detection systems for mid-high orbit targets during daytime， the extremely low SNR （Signal-to-Noise Ratio） of mid-high orbit imaging necessitates the use of multi-frame joint correlation detection techniques for target extraction. To address the challenge of target extraction caused by stellar pIn ground-based optical detection systems for mid-high orbit targets during daytime， the extremely low SNR （Signal-to-Noise Ratio） of mid-high orbit imaging necessitates the use of multi-frame joint correlation detection techniques for target extraction. To address the challenge of target extraction caused by stellar paths， this paper introduces a stars removal algorithm for single-frame images. The process involves generating a background image with replaceable stars from the original image， identifying suspected targets in a single frame based on stellar imaging characteristics， detecting real stellar targets through single-frame and multi-frame association methods， and replacing stellar targets in the original image with corresponding background pixels. Experimental results show the algorithm effectively removes stars with apparent magnitudes of 4 to 9， achieving an average removal success rate of 98%. After multi-frame joint correlation detection， target intensity remains unchanged， while stellar paths are significantly reduced. The algorithm processes a full-frame image in under 12ms， meeting the real-time requirements of a 25 Hz camera frame rate. This approach significantly enhances the extraction of mid-high orbit targets using multi-frame joint correlation detection..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3436 (2024)
Response-surface selection optimization of FEA parameter for two-dimensional space turntable
Haosu ZHANG, Zhenyu WANG, Yu CAO, Aihong SHANGGUAN... and Yongfan JIANG|Show fewer author(s)
To enhance the efficiency of reconnaissance and surveillance in space optical systems， reduce the mass and power consumption of onboard payloads， and optimize onboard resource allocation， a lightweight design for a two-dimensional turntable is developed to improve its payload ratio. This paper presents an optimization To enhance the efficiency of reconnaissance and surveillance in space optical systems， reduce the mass and power consumption of onboard payloads， and optimize onboard resource allocation， a lightweight design for a two-dimensional turntable is developed to improve its payload ratio. This paper presents an optimization method for selecting FEA parameter response surfaces tailored to two-dimensional turntables， enhancing mechanical simulation accuracy through the integration of FEA simulations and mechanical tests. First， an FE mechanical mesh model of the structure is established. Significant simulation parameters are identified as matching targets through significance analysis. The quadratic response surface coefficient matrix is then derived using the least square method， and the response surface's effectiveness is evaluated. Finally， the optimized model parameters are determined using an optimization algorithm. Utilizing the matched FE model， mechanical analysis of the two-dimensional turntable is conducted， and results for three structural responses below 100 Hz-modal frequency， low-frequency sinusoidal vibration acceleration response at characteristic nodes， and random vibration stress response at characteristic nodes-are obtained. Compared to dynamic tests， the maximum error in results decreased from 8% to 3%， 17% to 4.9%， and 8.5% to 2.2%， respectively. This approach significantly improves mechanical simulation accuracy， fulfilling engineering application requirements. It has been successfully applied to the development of a two-dimensional turntable， increasing its payload ratio from 0.6 in the previous generation to 1.6..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3446 (2024)
Aero-engine nacelle acoustic hole detection system integrating improved semi-supervised segmentation method
Qingyu DONG, Biao MEI, Yun FU, Rongjin YANG, and Weidong ZHU
To address the need for large-scale drilling of acoustic holes in aero-engine nacelle composite liners， as well as challenges posed by complex material surfaces and the small size and high density of holes， a visual detection system was developed for a robotic multi-spindle drilling system. To overcome the lack of labeTo address the need for large-scale drilling of acoustic holes in aero-engine nacelle composite liners， as well as challenges posed by complex material surfaces and the small size and high density of holes， a visual detection system was developed for a robotic multi-spindle drilling system. To overcome the lack of labeled data for composite acoustic holes， a semi-supervised segmentation method was introduced for precise segmentation. Based on these results， a reference hole detection scheme was designed using a geometric parameter fitting algorithm to accurately identify reference holes prior to drilling. Porosity detection was achieved using a porosity calculation formula combined with segmentation outcomes. A visual detection system， integrating LabVIEW and Python， was developed to automate the detection of acoustic hole porosity and reference holes.Tests on composite liner samples demonstrated that the improved semi-supervised method reduces labeled data requirements by 70% while achieving an mIoU of 95.70%. Training and detection parameters were significantly reduced. The visual detection system， incorporating the semi-supervised method， achieved a porosity detection variance of only 0.023% compared to manual results and demonstrated higher accuracy in reference hole detection.The visual detection system satisfies the high-quality manufacturing standards of aero-engine nacelles and meets efficiency demands for drilling and detection. The integration of semi-supervised methods into the system is a significant advancement for multi-spindle robotic drilling in scenarios with limited labeled data..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3457 (2024)
Precision control on composite axis of lightweight quantum tracking system
Shuaibei YU, Yanbo CAO, Qiang FEI, Fang WANG, and Jingxu SUN
Satellite-to-ground laser communication utilizing quantum encryption has emerged as a novel method for long-distance information transmission， offering higher bandwidth and enhanced data security compared to traditional RF systems. This approach is characterized by its high bit rate， miniaturization， and low power consSatellite-to-ground laser communication utilizing quantum encryption has emerged as a novel method for long-distance information transmission， offering higher bandwidth and enhanced data security compared to traditional RF systems. This approach is characterized by its high bit rate， miniaturization， and low power consumption. To establish a reliable laser communication link between satellites and ground terminals， precise tracking and alignment between the ground optical terminal and the satellite optical payload must be maintained within the designated time window. This requirement is particularly challenging as the ground optical terminal operates under complex environmental conditions and various disturbances. Therefore， the design of a lightweight， high-precision ground laser tracking terminal， coupled with a stable and reliable tracking control algorithm， is essential.In this study， we propose a composite-axis tracking system incorporating dual detectors. The optical terminal is designed with a lightweight structure， utilizing a large-aperture reflective imaging system with an integrated， compact architecture and lightweight materials， achieving a weight reduction of over 50% compared to traditional designs. A two-stage tracking control system is implemented， combining a gimbal and a fast steering mirror. The control system integrates parameter identification and auto-tuning optimization using a PCA-NN intelligent algorithm， reducing the PV （peak-to-valley） tracking error within the time window from over 20″ to less than 5″. This approach effectively addresses the challenges associated with lightweight laser communication tracking systems， providing a robust platform for broader applications in interstellar laser communication technologies and meeting the stringent requirements of quantum-encrypted laser communication..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3469 (2024)
Real-time deblurring of wideband small target based on attention mechanism
Deyan ZHU, Yongqi AO, Jiayi XU, Chengcheng LI, and Yufan ZHANG
The detection and recognition of long-distance moving targets on space-based platforms face challenges such as wide bandwidth， blurry images， and small target sizes. To address these issues， a multi-scale， multi-stage convolutional neural network incorporating an attention mechanism is proposed， meeting the demands forThe detection and recognition of long-distance moving targets on space-based platforms face challenges such as wide bandwidth， blurry images， and small target sizes. To address these issues， a multi-scale， multi-stage convolutional neural network incorporating an attention mechanism is proposed， meeting the demands for high real-time performance， generalization， and deblurring quality. The approach employs a lightweight multi-scale， multi-stage network with an optimized module count to ensure real-time processing， integrating a frequency-domain-based self-attention solver， a discriminative FFN （F-D）， and a convolutional block attention module （CBAM） to extract critical spatial and frequency domain information. The experimental results show a deblurring restoration rate exceeding 34 frame/s， with time consumption reduced to one-third of conventional methods. On the infrared small target dataset， the PSNR exceeds 32 dB and the SSIM surpasses 0.87， while on the visible light small target dataset， the PSNR exceeds 17 dB and the SSIM surpasses 0.93.The algorithm demonstrates strong generalization across wideband scenarios， effectively restoring the contours and shapes of small targets..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3479 (2024)
Single target tracking in complex scenarios
Huilan LIN, Chunlei ZHAO, Zhicheng HAO, Shi LIU... and Junqiang ZHANG|Show fewer author(s)
To address challenges in single-target tracking under complex scenarios such as target deformation， occlusion， similar interference， and out-of-view situations， a novel tracking algorithm is proposed. Building on the Staple algorithm， the method optimizes pixel weight assignment using a two-dimensional Gaussian functioTo address challenges in single-target tracking under complex scenarios such as target deformation， occlusion， similar interference， and out-of-view situations， a novel tracking algorithm is proposed. Building on the Staple algorithm， the method optimizes pixel weight assignment using a two-dimensional Gaussian function and enhances the color histogram to improve target-background distinguishability. An adaptive fusion mechanism based on the Peak Side Lobe Ratio （PSR） is introduced to combine HOG and color features， with carefully selected fusion coefficients ensuring feature reliability. The target's optimal center position is determined by analyzing the distance between the current and previous frame centers， alongside the maximum composite response， effectively mitigating interference from similar targets. Target loss or occlusion is identified using composite response， HOG features， and Average Peak-to-Correlation Energy （APCE）， maintaining the target frame's position and enabling timely re-tracking upon reappearance. A template update strategy combining past and current frame information further enhances tracking accuracy. Tests on the OTB100 dataset with deformation， occlusion， and out-of-view scenarios show that the improved algorithm increases overall and specific attribute success rates （deformation， occlusion， out-of-view） by 1.8%， 3.3%， 2%， and deformation precision by 9% compared to the Staple algorithm. On the VOT16 dataset， the overlap rate for overall and occlusion attributes improves by 0.022 2 and 0.019 6 respectively， meeting the demands of target tracking in complex scenarios..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3490 (2024)
Specral-spatial classification of hyperspectral imagery with hybrid architecture of 3D-CNN and Transformer
Haizhao JING, Lijie TAO, and Haokui ZHANG
To address pixel-level land cover classification in hyperspectral images （HSI）， a hybrid model 3D-ConvFormer is proposed. The model integrates 3D convolutional neural networks （3D-CNN） and self-attention mechanisms to effectively extract spatial-spectral features. In the shallow layers， 3D-CNN operations capture local To address pixel-level land cover classification in hyperspectral images （HSI）， a hybrid model 3D-ConvFormer is proposed. The model integrates 3D convolutional neural networks （3D-CNN） and self-attention mechanisms to effectively extract spatial-spectral features. In the shallow layers， 3D-CNN operations capture local spatial-spectral features， while in the deeper layers， the self-attention mechanism operates within convolutional windows to enhance feature extraction flexibility. This design achieves a synergistic fusion of the translation invariance of convolutional networks and the adaptive feature extraction capabilities of self-attention. The model's performance was evaluated on three publicly available hyperspectral image datasets—Indian Pines， PaviaU， and WHU-Hi-Longkou—using three metrics： Overall Accuracy （OA）， Average Accuracy （AA）， and the Kappa coefficient. Experimental results demonstrate that the proposed model achieved an OA of 98.41%， AA of 97.56%， and Kappa of 98.16% on the Indian Pines dataset； an OA of 99.39%， AA of 99.30%， and Kappa of 99.18% on the PaviaU dataset； and an OA of 98.53%， AA of 98.97%， and Kappa of 98.06% on the WHU-Hi-Longkou dataset. Compared to baseline models， 3D-ConvFormer consistently outperformed in classification tasks across all three datasets， significantly improving the accuracy of hyperspectral image classification..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3504 (2024)
Review of sub-pixel edge detection algorithms
Mengjie ZENG, Chenxi WANG, Junjie LAI, Yihan CHEN... and Hongliang REN|Show fewer author(s)
This paper reviews the research status and application progress of sub-pixel edge detection algorithms. First， three traditional edge detection algorithms （gradient-based， statistical-based， and structured edge detection algorithms） are briefly introduced， and their limitations in edge detection accuracy are analyzed. This paper reviews the research status and application progress of sub-pixel edge detection algorithms. First， three traditional edge detection algorithms （gradient-based， statistical-based， and structured edge detection algorithms） are briefly introduced， and their limitations in edge detection accuracy are analyzed. Then， the concept of sub-pixel edge detection and its advantages in improving edge detection precision are elaborated. Subsequently， three major sub-pixel edge detection methods （interpolation method， fitting method， and moment method） are discussed in detail regarding their theoretical foundations， algorithmic principles， and application characteristics. Comparative analysis shows that these methods can achieve high-precision edge detection in different application scenarios. Finally， the paper summarizes the main existing challenges in sub-pixel edge detection technology and provides future research prospects from aspects of noise suppression， modal optimization， and technology integration..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3513 (2024)
Computed tomography image segmentation of cell pole piece via strip attention mechanism
Zefang LIU, Chao LONG, Xueshuan LIU, Yan HAN... and Liming DUAN|Show fewer author(s)
The segmentation of pole pieces in CT images is a crucial step in utilizing industrial computed tomography （CT） to detect the pole pieces of laminated cells. However， the complex structure and high aspect ratio of laminated cells pose challenges for existing segmentation methods， which struggle to meet the speed and acThe segmentation of pole pieces in CT images is a crucial step in utilizing industrial computed tomography （CT） to detect the pole pieces of laminated cells. However， the complex structure and high aspect ratio of laminated cells pose challenges for existing segmentation methods， which struggle to meet the speed and accuracy demands of large-scale production. To address this， this paper introduces a novel CT image segmentation network based on a strip attention mechanism. The proposed network comprises a lightweight backbone （MobilenetV2）， a Strip Atrous Spatial Pyramid Pooling （S-ASPP） module， and a decoder module. MobilenetV2 reduces network parameters and enhances segmentation speed， while the S-ASPP module employs strip pooling to retain strip feature information， effectively mitigating under-segmentation issues. The strip attention mechanism in the decoder focuses on edge and detail information， improving edge sharpness and detail clarity. Experimental results demonstrate that the proposed network achieves an average pixel classification accuracy （mAcc） of 98.62%， an average intersection over union （mIoU） of 89.97%， a parameter count of 5.814M， and a segmentation speed of 56.94 frame/s. Compared to DANet， DeepLabV3+， U-Net， HRNet， and Segnext， the proposed method achieves the highest segmentation accuracy and outperforms DANet， DeepLabV3+， U-Net， and HRNet in speed， with slightly lower speed than Segnext. Considering mIoU， mAcc， FPS， and parameter count comprehensively， the proposed network significantly enhances segmentation precision and efficiency while maintaining low computational cost， outperforming mainstream segmentation networks..
Optics and Precision Engineering
- Publication Date: Dec. 10, 2024
- Vol. 32, Issue 23, 3525 (2024)
