Chinese Version Light学术出版中心
Latest Issue
Issue 6 ， 2023
-
Simultaneous three-channel photometric system of 1.2 m photoelectric telescope
Abstract：Multicolor CCD photometric technology has been widely used in astronomical observations owing to its practicability， simplicity， and effectiveness； however， the issue of simultaneity needs to be addressed. In this paper， a new type of simultaneous three-channel photometric system， which uses a dichroic mirror to realize light splitting in the g′， r′， and i′ bands of the Sloan Digital Sky Survey （SDSS）， is introduced. First， the optical system of the three-channel photometer was simulated and analyzed by Zemax. The results showed that the system satisfied the overall design criteria and met the requirements for its intended application. Then， we observed a large number of SDSS standard stars to evaluate the optical performance of the equipment. The results indicated that the equipment could achieve simultaneous imaging within a field of view of 21.5′×21.5′， 21.5′×21.5′， and 21.3′×21.3′ in the g′， r′， and i′ channels， respectively. The system efficiency for each channel was 65.6， 68.3， and 63.7%， respectively， and the limiting magnitude of each channel was 15.26， 16.39， and 15.63， respectively， when the exposure time and signal-to-noise ratio were 1 s and 5， respectively. In the future， we will improve the detection capability of limiting magnitudes by optimizing the system.Keywords：simultaneous three-channel photometric system;three-channel photometer;Sloan Digital Sky Survey（SDSS）;optical performance- 158
- |
- 46
- |
- 0
Published：2023-03-25 -
Development and application of high-performance cone-beam industrial X-ray CT system
Abstract：Improving the technical performance of the industrial computed tomography （CT） testing system has always been the common objective of the system’s R&D personnel and users in the field of radiographic nondestructive detection. To this end， a CT image detection method using a scintillation screen coupled with a scientific level-filter array complementary metal–oxide–semiconductor （CMOS） camera is proposed. The important engineering details associated with the hardware and software development of the micro focus industrial CT system using this method are introduced. The comparison results of the measured technical parameters of the independently developed anti-radiation X-ray digital camera and those of the commercial flat panel detector are presented. In addition， the comparison results of the computed efficiency of the CT reconstruction algorithm of the X-ray CT integrated software （CTER MAX） and that of an equivalent commercial software， as well as the detection results of the CT system spatial resolution are described. The results indicate that the anti-radiation X-ray digital camera has advantages including ultra-high pixel sampling， low noise， high dynamic range， high resolution， radiation resistance， and low cost. In terms of spatial resolution， it has more scope for improvement than the flat panel detector and is more adaptable to meeting the needs of more application scenarios. The CTER MAX software has complete functionality and is user-friendly. Compared with the commercial software， it has approximately 4.5 times faster computing rate. The comprehensive spatial resolution of the CT system is 5 μm， which is equivalent to the focal spot size of the radiation source. The system is stable and reliable， and it can be extensively used in various industrial nondestructive detection fields， such as the detection of defects in aircraft engine leaves （or blades） and rocks.Keywords：Industrial CT;digital X-ray camera;non-destructive detection;cone beam;Image- 80
- |
- 22
- |
- 0
Published：2023-03-25 -
Optimization and reconstruction of progressive addition free-form surface lens
Abstract：The rear surface of a freeform progressive addition lens （PAL） is often fitted using multiple surfaces， and curvature changes at the joints are usually large and discontinuous. Initial freeform PALs frequently required local or global optimization to improve their optical properties and surface smoothness. A global fitting method based on the non-uniform rational B-spline curve and least squares curve is proposed to optimize reconstruction of the rear surface of a lens. The algorithm principle of least squares curve fitting is introduced， and the rear surfaces of two sets of lenses with different powers are reconstructed under the same parameters； moreover， their optical properties are simulated， quantified， and evaluated. Experimental results demonstrate that the maximum astigmatism of both lens groups reduces by 0.61 D and 0.45 D， respectively. The astigmatism in the distance area decreases by 0.15 D and 0.16 D， respectively， and the middle channel width （Y = 0 mm） increases by over 1.8 mm. The proposed reconstructed surface improves the rear surface smoothness， gradient channel width， and effectively reduces PAL maximum astigmatism.Keywords：non-uniform rational B-spline;astigmatism;freeform surface;progressive addition;reconstruction- 134
- |
- 18
- |
- 0
Published：2023-03-25
Modern Applied Optics
-
Status and prospect of opto-mechanical integration analysis of micro-vibration in aerospace optical cameras
Abstract：With the continuous development and progress of China’s aerospace industry， aerospace optical cameras are becoming more lightweight with large apertures， large fields of view， and high resolution， which results in higher requirements for their design and analysis. The focal length and optical aperture of optical systems continue to increase， whereas the stiffness of optical mechanical systems is limited by mass. They are also increasingly sensitive to micro-vibrations caused by the normal operation of moving satellite parts in orbit. The micro-vibration of space optical cameras affects their imaging quality in orbit. Therefore， in recent years， satellite micro vibration and its control have attracted increasing attention. Based on discussions and analyses of the optical mechanical integration analysis methods of aerospace optical cameras at home and abroad， the key technologies and development directions of optical mechanical integration analysis are discussed. In view of the limitations of optical mechanical integration analysis in China， this study proposes the idea of establishing a mathematical model of the entire link of micro-vibration transmission and then constructing the degradation mechanism of the micro-vibration image quality of space optical cameras.Keywords：micro-vibration;aerospace optical camera;linear state space;opto-mechanical integration- 163
- |
- 40
- |
- 0
Published：2023-03-25 -
Lapping performance of fixed silicon-based agglomerated diamond abrasive pad
Abstract：Typically， abrasive microfracture is a predominant factor affecting fixed abrasive （FA） pad performance. Moreover， the compositions and bonding strengths of the binder determine its microfracture behavior. To realize a highly efficient lapping process， the preparation of silicon-based agglomerated diamond （SAD） abrasives and the effect of the binder composition on lapping performance were investigated. SAD abrasives with various silicon content binders were prepared at 840 ℃， 880 ℃， and 920 ℃， and their micromorphologies were observed using a scanning electron microscope. Lapping tests were conducted on a K9 specimen， and the lapping performance of the aforementioned FA pads at loads of 7， 14， and 21 kPa was compared. The higher the sintering temperature and silicon content of the binder， the more uniform the binder filling， the more reasonable the pore distribution， and the more evident the microbreakage during the SAD grinding process. Under the 21 kPa load， the material removal rate （MRR） of the FA pad with SAD abrasives possessing the highest silicon content and sintered at 920 ℃ was the highest reaching 63.32 μm/min， while the Ra was approximately 0.515 μm. Under the 7 kPa lapping load， the average surface roughness of a workpiece lapped by an FA pad with SAD abrasives possessing the lowest silicon content and sintered at 920 ℃ was the lowest reaching approximately 0.182 μm， while the MRR was 7.89 μm/min. Efficient lapping of K9 optical glass can be achieved using a consolidated SAD abrasive pad.Keywords：high-efficiency lapping;fixed abrasives pads;agglomerated diamond abrasives;silicon-based bind- 55
- |
- 12
- |
- 0
Published：2023-03-25 -
Femtosecond laser precision machining of fuel injection holes in nickel-based superalloys
Abstract：Femtosecond laser planar spiral drilling strategies offer several advantages， such as flexibility， precision， and process efficiency， making it possible to achieve high machining accuracy for specific types of aerospace engine oil supply nozzle. Therefore， in this study， we adopted a femtosecond laser helical drilling method and systematically investigated the effects of laser power， single layer feed， single layer scan time， defocusing amount， and pulse repetition rate on the quality of drilling. The defocusing amount has significant influence on the inlet diameter， while power and feed are the parameters affecting the outlet diameter the most； processing efficiency heavily depends on power. Considering these aspects， through-holes with a hole size of 390 μm were successfully prepared on GH3044 sheets with a thickness of 1.5 mm. The errors for inlet-outlet diameters and taper were less than 0.6 μm and 0.5°， respectively. Using the statistical analysis of the variation law of hole wall roughness under different parameters， it was found that the roughness value can be effectively reduced to Sa = 0.6 μm， which exceeds than the process requirement， by simply adjusting parameters. Moreover， the analysis of the hole wall morphology showed that the drilling quality was excellent. By increasing the laser pulse repetition frequency， the microstructure near the hole wall gradually coarsens. However， there is no obvious recast layer or heat-affected zone， which is necessary to realize the relatively “cold” processing for high-precision micro holes. This study provides a theoretical basis for the fabrication of special-shaped micro holes using the high atomization effect.Keywords：laser technology;femtosecond laser;nickel base superalloy;micro hole;processing quality- 73
- |
- 19
- |
- 0
Published：2023-03-25
Micro/Nano Technology and Fine Mechanics
-
Multi-object pedestrian tracking method based on improved high resolution neural network
Abstract：This study proposes an improved high-resolution neural network to address the issue of detection and tracking failures caused by target blockage in a multi-target pedestrian tracking process. First， to enhance the initial feature extraction capability of the network for pedestrian targets， a second-generation bottleneck residual block structure was introduced into the backbone of a high-resolution neural network， thus improving the receptive field and feature expression capability. Second， a new residual detection block architecture with a two-layer efficient channel attention module was designed to replace the one at the multi-scale information exchange stage of the original network， thus improving the test performance of the entire network system. Finally， the network was fully trained by selecting appropriate parameters， and subsequently， the algorithm was tested using multiple test sets. The test results indicated that the tracking accuracy of the proposed algorithm was 0.1%， 1.6%， and 0.8% higher than that of FairMOT on 2DMOT15， MOT17， and MOT20 datasets， respectively. In conclusion， the proposed algorithm-tracking stability for longer video sequences was greatly improved. Therefore， it can be applied to special scenarios with more targets and occlusion area.Keywords：ID switch;high-resolution feature extraction network;Efficient Channel Attention（ECA）;Bottle2neck;FairMOT- 101
- |
- 20
- |
- 0
Published：2023-03-25 -
Single heading-line survey of MGTS for magnetic target pattern recognition
Abstract：The planar grid measurement of the magnetic gradient tensor system （MGTS） is often utilized for magnetic target recognition； however， it is difficult to measure， complicated to analyze， and requires high instrument precision. In this regard， we propose a magnetic target pattern recognition method based on MGTS single heading-line survey. First， the sensitivity of magnetization direction is analyzed for 15 attributes including the components， eigenvalues， and invariants of the magnetic gradient tensor （MGT）. The more sensitive attributes are used to identify target postures， and the insensitive ones are for target shapes. Then， the time-domain signal characteristics of the measured quantities are extracted and the category labels are set. Principal component analysis （PCA） is employed to reduce dimensionality， visualize features， and determine the optimal dimension. Finally， the kernel extreme learning machine optimized by the sparrow search algorithm （SSA-KELM） is used to train and test the survey sample data. The pattern recognition of the magnetic target is hence realized. In the simulation， the recognition of 1） different magnetization direction categories of magnetic dipoles and 2） shape categories of geometric bodies such as the sphere， cuboid， and cylinder is 100% accurate. In the experiment， a total of 180 learning routes were measured for three types of magnets and their corresponding postures. Under the training：testing ratio of 6：4， the results of magnet posture and shape recognition were completely accurate.Keywords：Magnetic Gradient Tensor System（MGTS）;Single heading-line survey;magnetic target pattern recognition;sparrow search algorithm;kernel extreme learning machine- 34
- |
- 9
- |
- 0
Published：2023-03-25 -
Ship detection for complex scene images of space optical remote sensing
Abstract：When deep-learning-based target detection algorithms are directly applied to the complex scene images generated by space optical remote sensing （SORS）， the ship target detection effect is often poor. To address this problem， this paper proposes an improved YOLOX-S （IM-YOLO-s） algorithm， which uses densely arranged offshore ships with complex backgrounds and ships with multi-interference and small targets in the open sea as detection objects. In the feature extraction stage， the CA location attention module is introduced to distribute the weight of the target information along the height and width directions， and this improves the detection accuracy of the model. In the feature fusion stage， the BiFPN weighted feature fusion algorithm is applied to the neck structure of IM-YOLO-s， which further improves the detection accuracy of small target ships. In the training stage of model optimization， the CIoU loss is used to replace the IoU loss， zoom loss is used to replace the confidence loss， and weight of the category loss is adjusted， which increases the training weight in the densely distributed areas of positive samples and reduces the missed detection rate of densely distributed ships. In addition， based on the HRSC2016 dataset， additional images of small and medium-sized offshore ships are added， and the HRSC2016-Gg dataset is constructed. The HRSC2016-Gg dataset enhances the robustness of marine ship and small and medium-sized pixel ship detection. The performance of the algorithm is evaluated based on the dataset HRSC2016-Gg. The experimental results indicate that the recall rate of IM-YOLO-s for ship detection in the SORS scene is 97.18%， AP@0.5 is 96.77%， and the F1 value is 0.95. These values are 2.23%， 2.40%， and 0.01 higher than those of the original YOLOX-s algorithm， respectively. This indicates that the algorithm can achieve high quality ship detection from SORS complex background images.Keywords：ship detection;deep learning;coordinate attention;weighted feature fusion;loss function optimization- 67
- |
- 27
- |
- 0
Published：2023-03-25 -
Vehicle detection method based on remote sensing image fusion of superpixel and multi-modal sensing network
Abstract：A remote sensing image vehicle detection method combining superpixels and a multi-modal perception network is proposed with the purpose of reducing recognition accuracy due to background interference， target density， and target heterogeneity in remote sensing image vehicle detection. First， based on the region merging rules of hybrid superpixels， the superpixel bipartite graph fusion algorithm was used to fuse the superpixel segmentation results of the two modalities， which improved the accuracy of the superpixel segmentation results of different modal images. Second， MEANet， a vehicle detection method of remote sensing images based on a multi-modal edge aware network， was proposed. An optimized feature pyramid network module was introduced to enhance the ability of the network to learn multi-scale target features. Finally， the two sets of edge features generated by the superpixel and multi-modal fusion module were aggregated through the edge perception module， and the accurate boundary of the vehicle target was generated. Experiments were conducted on the ISPRS Potsdam and ISPRS Vaihingen remote sensing image datasets， and the final scores were 91.05% and 85.11%， respectively. The experimental results showed that the method proposed in this study has good detection accuracy and good application value in high-precision vehicle detection of multi-modal remote sensing images.Keywords：super pixel;multimodal perception;remote-sensing image;vehicle detector- 84
- |
- 23
- |
- 0
Published：2023-03-25 -
Deep learning image denoising based on multi-stage supervised with Res2-Unet
Abstract：To restore high quality images from different types of noise images， this study developed a multi-stage supervised deep residual （MSDR） neural network based on Res2-Unet-SE. First， using the neural network， the image denoising task was devised as a multi-stage process. Then， in each processing stage， image blocks with different resolutions were input into a Res2-Unet sub-network to obtain feature information at different scales， and an adaptive learning of the feature fusion information was transferred to the next stage through a channel attention mechanism. Finally， the feature information of different scales was superimposed to achieve high-quality image noise reduction. The BSD400 dataset was selected for training in the experiments， and a Gaussian noise reduction test was performed using the Set12 data set. Real noise reduction test was conducted using the SIDD data set. Compared with the common denoising neural network， the peak signal-to-noise ratios （PSNRs） of the proposed denoising convolutional neural network （DnCNN） improved by 0.03 dB， 0.05 dB， and 0.14 dB when Gaussian noises of σ = 15， 25 and 50， respectively， were added to the image data set. Compared with the latest dual residual block network （DuRN） algorithm， the PSNR of the image denoised using the proposed algorithm was higher by 0.06 dB， 0.57 dB， and 0.39 dB， respectively. For images containing real noise， the PSNR of the image denoised by the proposed algorithm was 0.6 dB higher than that by the convolutional blind denoising network （CBDNET） algorithm. The results indicate that the proposed algorithm is highly robust in the task of image denoising， and it can effectively remove noise and restore the details of an image， as well as fully maintain the global dependence of the image.Keywords：image denoising;real noise;residual network;Channel attention mechanism;supervisory attention mechanism- 66
- |
- 22
- |
- 0
Published：2023-03-25 -
Bone scintigraphic classification method based on ACGAN and transfer learning
Abstract：Owing to the limited availability of samples and unbalanced categories of bone images， it is difficult to classify these images. To improve the classification accuracy of bone images， this study developed a bone-image classification method based on auxiliary classifier generative adversarial network （ACGAN） data generation and transfer learning. First， an multi-attention U-Net-based ACGAN （MU-ACGAN） model was designed to address the imbalance of bone-image categories. The model uses U-Net as the generator framework and combines dense residual connection and channel-spatial attention mechanism to improve the generation of bone-image detail features. The discriminator extracts bone-image features by using a dense residual attention convolution block for discrimination. Next， the amount of data was further expanded via combination with traditional data enhancement methods. Finally， a multi-scale convolutional neural network was designed to extract the features at different scales of bone imaging so as to improve the classification effect. In the model training process， a two-stage transfer learning method was adopted to optimize the initialization parameters of the model and address the problem of overfitting. Experimental results indicate that the classification accuracy of the proposed method reaches 85.71%， effectively alleviating the problem of low classification accuracy on small sample bone-image datasets.Keywords：bone scintigraphy;Auxiliary Classifier Generative Adversarial Networks（ACGAN）;transfer learning;attention mechanism;data augmentation- 54
- |
- 12
- |
- 0
Published：2023-03-25 -
Lightweight vehicle detection using long-distance dependence and multi-scale representation
Abstract：Vehicle detection based on deep learning plays a vital role in many fields. In recent years， it has presented a major development direction for computer vision. Lightweight vehicle detection includes the exploration of network structure and computing efficiency， and it is widely used in many fields such as intelligent transportation. However， challenges exist in different scenarios， such as large changes in vehicle scale in detection cameras and vehicles overlapping each other， which reduce the precision of the network in detecting vehicles. To solve these problems， this study proposes an improved YOLOv5s method for detecting vehicles. First， the study proposes to capture long-distance dependencies between objects through a visual attention network and apply new weights to the network’s original feature map to increase the adaptability of the network. These operations improve the anti-occlusion ability of the network. Second， the horizontal residual is constructedagain in the residual module. The output feature maps contain the same number and different sizes of receptive fields per module. Feature extraction occurs at a more fine-grained level， thereby enriching the multi-scale representation ability of the network. The experimental results show that the improved network provides 2.1% mAP performance on the Pascal visual object classes （VOC） vehicle telemetry dataset and a 1.7% mAP performance on the MS COCO vehicle telemetry dataset. The performance of the improved network is more powerful and its anti-occlusion ability is enhanced. Compared with the original network， the detection results are more competitive.Keywords：computer vision;vehicle detection;lightweight network;long-distance dependence;multi-scale representation- 34
- |
- 16
- |
- 0
Published：2023-03-25 -
Design of channel attention network and system for micro target measurement
Abstract：Microdevices are widely used in the electronic industry. However， the diffraction effect， which causes misalignments in the physical and optical edges of micro devices， brings challenges to detection and measurement. To address this issue， this study combines image super-resolution reconstruction with target measurement to propose an image super-resolution reconstruction algorithm based on edge enhancement and build a corresponding measurement system. In this study， a new quality evaluation parameter is proposed for image super-resolution reconstruction， to prove the feasibility of super-resolution reconstruction in improving target measurement accuracy. Aiming at the target edge， a channel attention mechanism is also introduced into the network to enhance its ability to reconstruct the image edge. Finally， the target measurement system is designed and built， and experiments are carried out. The results show that the proposed algorithm can achieve higher peak signal to noise ratio （PSNR） and structural similarity （SSIM） values on an open dataset. In real-world measurements， this algorithm improved the limit resolution of the original measurement system by 25.9% and the target measurement accuracy by 51.6%， on average. This study provides a potential direction for the development of micro-target detection and measurement in industrial production.Keywords：deep learning;image super-resolution;computer vision;edge detection;subpixel- 199
- |
- 62
- |
- 0
Published：2023-03-25
Information Sciences
Loading...
0