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Issue 8 ， 2023
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Synthetic photometry instrument magnitude estimation method of Gaia catalogue
Abstract：To meet the requirements of measuring the sensitivity of star trackers， establishing guide star catalogs， and capturing space targets， accurate instrument magnitudes are required as data support. In this study， the method of instrument magnitude calculation and photometric system are investigated. First， combined with the principle of instrument magnitude calculation， synthetic photometric method and color index fitting method are introduced. Then， the instrument magnitude calculation method of Gaia catalog based on synthetic photometry using GaiaDR2 catalog， that is， synthetic photometric calculation and color index model based on the G，GBP，GRP star system， is introduced as the data source. Subsequently， color index model accuracy of other instrument magnitude calculation methods is calculated. Finally， aperture photometry is performed on the in-flight image of the star tracker， and the actual instrument magnitude results are compared and verified with the calculated results. The calculation results of the GaiaDR2 catalog and Hipparcos catalog are compared with the actual magnitude. The results show that the standard deviations of the color index fittings using the calculated value and true value of GaiaDR2 are 0.003 5 and 0.037 6， respectively. The standard deviation of the calculated instrument magnitude difference （Mc-Mr） is 0.106 0， and the spectral coverage is relatively larger. Thus， it achieves high precision and can meet the needs of practical applications.Keywords：stellar instrument magnitude;Gaia catalogue;color index;synthetic photometry- 94
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Published：2023-05-04 -
Particle sorting with different refractive indices using single fiber optical tweezers
Abstract：In order to solve the problems encountered in classifying particles with different refractive indices， this paper proposes a parabolic fiber optic probe stretched by the fusion method； the resulting outgoing light field has different operating capabilities for particles with different refractive indices submerged in aqueous solutions， which can be used for particle classification. We couple a laser beam with a wavelength of 980 nm into the fiber optic probe and manipulate the fiber to achieve the capture and transport of particles and cells with three different refractive indices in liquid： silicon dioxide （SiO2）， polystyrene （PS）， and yeast cells， and thus achieve the classification of different particles in the range of 1-10 μm. The different capture capabilities of this parabolic fiber optic probe for the three particles were simulated， and the obtained theoretical and experimental results were in agreement. The use of this method to classify particles simplifies the experimental setup and has a wide range of potential applications in the development of label-free hybrid fiber optic sensors， infectious disease detection， and cell classification.Keywords：fiber optic tweezers;refractive index;cell screening;particle classification- 91
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Published：2023-05-04 -
Optical system design of calibration light source in 0.05-0.1 MPa wide atmospheric pressure environment
Abstract：Laser weapons would be an important means of strategic checks and balances for major powers in the future， and they can change the pattern of war. They usually use an adaptive optical （AO） system with Hartmann wavefront detection as feedback to improve the beam quality and ensure the damage effect. However， the refractive indices of air and glass materials change with the change in air pressure in the environment， introducing additional wavefront detection errors and making it difficult to ensure beam quality. The introduction of a calibrating light source can aid in calibrating the AO system in real-time， remove the system error， and ensure the correction effect； however， this method requires the absolute stability of the calibration light source. Therefore， a calibration light source suitable for a wide barometric pressure range is designed. First， based on the influence of air pressure on the optical system， by analyzing the relationship between the image surface displacement coefficient and the refractive index of the material， a method for rationally matching the optical materials and assigning optical focus is used to compensate for the influence of air pressure on beam quality. Thereafter， the simulation model of the optical system is constructed to analyze the stability and tolerance distribution of the wave aberration of the system， establishing a basis for the construction of the experimental platform. Finally， an experimental platform is established to verify the design results. According to the results， when the air pressure is in the range of 0.05-0.1 MPa， compared with that at the working point of 0.1 MPa， the wave aberration first decreases and then increases， reaching the minimum at 0.08 MPa， while the wave aberration PV value changes between 0.087 4λ and 0.019 8λ， with the maximum change being 0.067 6λ. Thus， the stability of the calibrated light source is satisfied， and effective compensation for pressure changes is realized.Keywords：laser weapon;adaptive optics;calibration light source;wide pressure;wavefront aberration- 107
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Published：2023-05-04
Modern Applied Optics
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Design of an optoelectronic integrated chip for reflective encoder application
Abstract：This study designed an optoelectronic chip， integrating a photodiode array， transimpedance amplifier， fully differential amplifier， and bias circuit， to meet the application requirements of reflective photoelectric encoders. First， the photodiode array was designed according to the imaging principle of the reflective encoder. The adjustable gain transimpedance amplifier and the fully differential driver amplifier were then cascaded. Regarding the signal processing circuit， it could increase load capacity in addition to reducing the noise of the readout signal. Subsequently， the design integrated the bias circuit to provide a wide power supply voltage input range and effective power supply ripple rejection. The whole chip was fabricated based on a 0.35-um photoelectric CMOS process. By building a test environment， the photoelectric chip can work normally in the wide power supply voltage range of 3.5-6 V， and the incremental signal output within 6000 r/min has good orthogonality. The results of angle measurement showed that the maximum error of angle measurement is 4.752″ and 5.04″ under forward and reverse rotations， respectively. Using a 5 V supply voltage， the DC power consumption of the circuit is 66.5 mW. The overall chip area is 5.91 mm×2.81 mm. In general， the proposed optoelectronic chip can meet the requirements of high integration of photoelectric chips， good signal orthogonality， wide power supply voltage input range， and high power supply ripple suppression ability. Therefore， it is suitable for reflective photoelectric encoders.Keywords：optoelectronic chip;reflective encoder;incremental signal;bias circuit;adjustable gain amplifier- 98
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Published：2023-05-04 -
A method for improving absolute positioning accuracy of industrial robot in entire workspace domain based on stereo vision
Abstract：The absolute positioning accuracy of industrial robots is low. At present， kinematics calibration based on the motion loop method is often used to improve the absolute positioning accuracy of the manipulator. However， the size of the calibration space limits the absolute positioning accuracy of the robot's entire workspace. To solve this problem， a method is proposed to improve the absolute positioning accuracy of the robotic arm in the entire workspace domain using the fusion of stereo vision and two- dimensional checkerboard targets. First， the industrial robot is rotated on a single axis and a stereo camera is used to record the single-axis rotation trajectory. The Rodrigues rotation is then used to perform three-dimensional curve fitting to obtain the rotation axis of the industrial robot. Second， a joint coordinate system is established according to the D-H rule and the kinematic parameters of the industrial robot are calculated according to the relative positional relationship between the joint coordinate systems. Third， the reduction ratio of the joint motor is calibrated by analyzing the relationship between the actual rotation angle of the joint and the output value of the encoder. Finally， the proposed method is verified by experiments and the results are compared with those of other accuracy improvement methods. The experimental results show that the absolute positioning accuracy of the manipulator is improved by 67% after calibration using the method in this study and that the effect is the same inside and outside the calibration space， which can improve the absolute positioning accuracy of the industrial robot in the entire working space domain.Keywords：industrial robot;kinematic calibration;deceleration ratio calibration;visual measurement- 106
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Published：2023-05-04 -
Development and application of full gas hydrostatic ring polishing machine
Abstract：Considering key challenges that include controlling the surface shape of a single-axis polishing machine， greater reliance on manual experience， and the necessity to meet demands such as the increasing number and precision of optical elements， a new ultra-precision ring polishing machine is proposed. First， a material removal model based on the Preston formula and Hertz contact theory was constructed by introducing the thrown disc runout as variable. Model simulation analysis shows that runout error variation leads to uneven removal of materials， and that the runout error of the spindle and the linear error of the guide rail will directly affect the surface shape correction and measurement accuracy of the disc. Based on this conclusion， the machine tool overall layout and beam support form were optimized using static， modal and harmonic response analysis. At the same time， the hydrostatic guide rail and spindle were designed to improve linear motion and spindle rotation accuracy. Finally， to verify the machine tool accuracy and machining index， the motion errors of the guide rail and turntable are better than 1.2 μm/400 mm and 0.4 μm， respectively， using the LK-G5000 laser sensor. ⌀ 300 mm UBK7 optical element in polishing， roughness and PV value superior to eight hours after Ra0.56 nm and 1/10λ. Finally， the results demonstrate that the proposed ultra-precision ring polishing machine meets design requirements.Keywords：ultra-precision machine tool;ring polishing;aerostatic spindle;aerostatic guide;material removal model- 95
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Published：2023-05-04
Micro/Nano Technology and Fine Mechanics
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Research on multi size automobile rim weld detection and positioning system based on depth learning
Abstract：In order to realize the automation and intelligence of automobile rim production equipment， improve the production efficiency of automobile rims， and reduce labor costs， this paper proposes a multi size automobile rim weld detection and positioning system based on YOLOv5s algorithm. First， the image acquisition device captures the image of the multi size rim weld seam in actual production， builds the rim weld seam data set， and uses K-means algorithm to regenerate the anchor frame of the data set to improve the convergence speed and feature extraction ability of the network； Secondly， CBAM （Convolutional Block Attention Module， CBAM） mixed domain attention mechanism is introduced to improve the model's attention to the rim weld and reduce background interference； Then， EIOU （Efficient Intersection Over Union Loss， EIOU） frame position regression loss function is used to improve the accuracy of rim weld identification frame； Finally， ASFF （Adaptive Spatial Feature Fusion， ASFF） adaptive feature fusion network is added to enable the target detection model to perform spatial filtering on multiple levels of features. The experimental results show that the accuracy and mAP0.5 of the improved algorithm are 98.4% and 99.2% respectively， which are 4.5% and 3.7% higher than the original YOLOv5s algorithm. The trained model is accelerated and deployed on the industrial personal computer using the reasoning acceleration framework TensorRT， and forms an interactive and display platform with the visual inspection software and the industrial touch screen. Through the verification of 3 000 wheel rim welds of different sizes in multiple batches in the actual production environment， the leakage rate is about 0.5%， which meets the requirements of automobile enterprises for the detection accuracy of multi size wheel rim welds.Keywords：weld line;loss function;YOLOv5s- 139
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Published：2023-05-04 -
Saliency maps for point cloud recognition models
Abstract：The specific properties of point cloud structures lead to difficulties in the interpretation of features learned from deep neural networks （DNNs）. In this study， a method is proposed to obtain saliency maps for point cloud target recognition models. First， a number of free factors are randomly released in the point cloud space and input into the model. Then， based on the designed contribution evaluation index， the pooled features output by the backbone are deflected as much as possible using the target point cloud recognition process based on gradient descent， and the factor positions are updated. The iterated factors do not participate in the recognition process， thus contributing "zero" to the prediction of the model such that moving the points in the target point cloud to the positions of these factors has exactly the same effect on the recognition result as dropping the points. The process of moving the points is differentiable， and the saliency maps can be obtained from the gradient information. The saliency maps for PointNet were generated on ModelNet40 using the proposed method. This method has a strong theoretical basis for generating saliency maps and is applicable to more number of datasets compared with the method using point cloud centers. The effect of shifting points to the no-contribution factor position is more similar to dropping points than shifting points to the center of the point cloud. In this study， dropping points by the saliency scores rapidly reduced the overall accuracy of the model from 90.4% to 81.1% with only 100 points dropped. Meanwhile， the saliency scores displayed good generality as evaluated using deep graph convolutional neural network （DGCNN） and PointMLP. The proposed method is driven by the model without prior assumptions and generates significance scores with higher precision； it is applicable to most point cloud recognition models. The results of saliency analysis are instructive for the construction of target recognition networks and data augmentation.Keywords：point cloud;saliency maps;target recognition;deep learning;interpretability- 104
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Published：2023-05-04 -
Blind light field image quality assessment combining disparity compensation with 3D data processing
Abstract：Compared with traditional 2D images， light field images （LFIs） can record the intensity and directional information of light in scenes. Accordingly， LFIs occupy an important position in multimedia technology applications. However， during the generation and transmission of LFIs， distortions are inevitably introduced that affect the user’s visual experience. Therefore， building an effective and accurate LFI quality assessment method to evaluate LFIs is necessary. Based on the two representations of LFIs， namely， pseudo-videos （PVs） and e-bipolar plane images （EPIs）， a blind LFI quality assessment method that combines disparity compensation and 3D data processing is proposed. First， a disparity compensation module is used to process the PV sequences of an LFI， and then the PV sequences following disparity compensation are processed using 3D-discrete wavelet transform and 3D-mean subtracted contrast normalized. The frequency and spatial features are then extracted. In addition， the features of a histogram of oriented gradients are extracted from the EPIs to represent the distortion of the angle information of the LFI. Finally， support vector regression is used to establish a regression model from an image quality assessment index to obtain a subjective quality score. Experimental results showed that the PLCC on the public databases of NBU-LF1.0， Win5-LID， and SHU reached 0.8861， 0.9287， and 0.9769， respectively. Compared with the classical 2D and advanced LFI image quality assessment methods， the proposed method has a higher consistency in terms of subjective quality assessment results.Keywords：light field image quality assessment;pseudo-video sequence;disparity compensation;angular consistency- 113
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Published：2023-05-04 -
Real-time semantic segmentation based on improved BiSeNet
Abstract：To improve the performance of image semantic segmentation on accuracy and efficiency for practical applications， in this study， we propose a real-time semantic segmentation algorithm based on improved BiSeNet. First， the redundancy of certain channels and parameters of BiSeNet is eliminated by sharing the heads of dual branches， and the affluent shallow features are effectively extracted at the same time. Subsequently， the shared layers are divided into dual branches， namely， the detail branch and the semantic branch， which are used to extract detailed spatial information and contextual semantic information， respectively. Furthermore， both the channel attention mechanism and spatial attention mechanism are introduced into the tail of the semantic branch to enhance the feature representation； thus the BiSeNet is optimized by using dual attention mechanisms to extract contextual semantic features more effectively. Finally， the features of the detail branch and semantic branch are fused and up-sampled to the resolution of the input image to obtain semantic segmentation. Our proposed algorithm achieves 77.2% mIoU on accuracy with real-time performance of 95.3 FPS on Cityscapes dataset and 73.8% mIoU on accuracy with real-time performance of 179.1 FPS on CamVid dataset. The experiments demonstrate that our proposed semantic segmentation algorithm achieves a good trade-off between accuracy and efficiency. Furthermore， the performance of semantic segmentation is significantly improved compared with BiSeNet and other existing algorithms.Keywords：semantic segmentation;attention mechanism;real time;deep learning- 72
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Published：2023-05-04 -
Survey of person re-identification based on human skeleton information
Abstract：The goal of person re-identification， a computer vision task， is to accurately match individuals across different camera views in a multi-camera surveillance system. This has broad applications in intelligent video surveillance， security， and other fields. Human skeleton information is a more robust discriminative feature compared to other human appearance features that can be easily changed. This paper primarily focuses on a pedestrian recognition method based on human body skeleton information to gain a deeper understanding of the current status of development in this field and assist researchers in further exploration. The proposed algorithm can be divided into independent and hybrid subtypes， where the hybrid subtype also includes RGB-D image and gait features in addition to the human body skeleton information. The different subtypes of the algorithm are subsequently compared and evaluated on established datasets. Finally， the problems and challenges of this study are summarized， and future development trends are proposed.Keywords：person re-identification;skeleton information;RGB-D images;gait feature- 77
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Published：2023-05-04 -
Low-light image enhancement algorithm based on light scattering attenuation model
Abstract：To address the problems of the over-enhancement and weak naturalness preservation ability of images in low-light environments with uneven illumination distribution， this study proposes a low-light image enhancement method based on the light-scattering attenuation model. First， a light-scattering attenuation model， which can explain the imaging process of non-uniform illumination distribution， is introduced. Second， the illumination map is initialized by estimating the illumination of each pixel using the Max-RGB filter. Third， according to the smooth attenuation characteristics of the illumination considering the incident distance between the segmented image areas， illumination components are optimized based on the depth and gradient data to obtain an accurate illumination map. Furthermore， constrain the estimation of the reflection component based on the local similarity possessed by the dark channel prior to transmittance. Finally， the low-light enhanced image is obtained by fusing the optimized illumination and reflection components using the proposed light-scattering attenuation model. The proposed method performs better than the NPE， LIME， HE， ALSM， MSRCR， MF， and WV_SIRE algorithms. In the case of the low-light image in different scenarios， a blind image quality assessment via anisotropy yielded values of up to 0.019 6 and cumulative probability of blur detection yielded values of up to 0.680 1 and the referenceless image spatial quality and natural image quality evaluators yielded minimum values of 21.471 5 and 2.699 5， respectively. The overall performance of the proposed method is better than those of the other algorithms. The experimental results confirm that the proposed method can effectively suppress noise， enhance image contrast and brightness， and well preserve image naturalness.Keywords：low-light image enhancement;light scattering attenuation model;depth information;dark channel prior- 83
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Published：2023-05-04 -
Design of intensity image and polarization image fusion network
Abstract：To overcome the disadvantage that the intensity image loses texture details in the dark， this study proposes a fusion method of the intensity and polarization images that combines the former with the polarization characteristics of the latter. First， an encoder network is constructed to extract the semantic information and texture details of the source image. Subsequently， the feature fusion network adopts an additive strategy and a residual network for image feature fusion. Finally， the fused image features are reconstructed through the decoder network to obtain the final fused image. Furthermore， according to the structural similarity loss and gradient loss between the source and fused images， this study proposes an improved loss function to guide the fusion network training. Experimental results indicate that compared with the modified dual-channel pulse coupled neural network （MD-PCNN）， which has the best fusion effect among the other six methods， the objective evaluation indicators of the proposed method—average gradient， information entropy， image quality， standard deviation， and improved multi-scale structural similarity—are improved by 4.3%， 1.0%， 8.1%， 2.5%， and 3.1%， respectively； the image noise is reduced by 8.8%. Moreover， the problem of losing texture details for intensity images is eliminated.Keywords：texture detail;residual network;structural similarity;image fusion- 125
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Published：2023-05-04
Information Sciences
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