2024
2024
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Record 157 of
Title:Simplified design method for optical imaging systems based on deep learning
Author Full Names:Xue, Ben(1,2); Wei, Shijie(1); Yang, Xihang(1); Ma, Yinpeng(1,2); Xi, Teli(1,3); Shao, Xiaopeng(4)Source Title:Applied OpticsLanguage:EnglishDocument Type:Journal article (JA)Abstract:Modern optical design methods pursue achieving zero aberrations in optical imaging systems by adding lenses, which also leads to increased structural complexity of imaging systems. For given optical imaging systems, directly reducing the number of lenses would result in a decrease in design degrees of freedom. Even if the simplified imaging system can satisfy the basic first-order imaging parameters, it lacks sufficient design degrees of freedom to constrain aberrations to maintain the clear imaging quality. Therefore, in order to address the issue of image quality defects in the simplified imaging system, with support of computational imaging technology, we proposed a simplified spherical optical imaging system design method. The method adopts an optical-algorithm joint design strategy to design a simplified optical system to correct partial aberrations and combines a reconstruction algorithm based on the ResUNet++ network to correct residual aberrations, achieving mutual compensation correction of aberrations between the optical system and the algorithm. We validated our method on a two-lens optical imaging system and compared the imaging performance with that of a three-lens optical imaging system with similar first-order imaging parameters. The imaging results show that the quality of reconstructed images of the two-lens imaging system has improved (SSIM improved 13.94%, PSNR improved 21.28%), and the quality of the reconstructed image is close to the quality of the direct imaging results of the three-lens optical imaging system. ? 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.Affiliations:(1) Xi’an Key Laboratory of Computational Imaging, School of Optoelectronic Engineering, Xidian University, Xi’an; 710071, China; (2) Advanced Optoelectronic Imaging and Device Laboratory, Hangzhou Institute of Technology, Xidian University, Hangzhou; 311200, China; (3) Guangzhou Institute of Technology, Xidian University, Guangzhou; 510555, China; (4) Xi’an Institute of Optics Precision, Mechanic of Chinese Academy of Sciences, Xi’an; 710119, ChinaPublication Year:2024Volume:63Issue:28Start Page:7433-7441DOI Link:10.1364/AO.530390數(shù)據(jù)庫ID（收錄號(hào)）:20244217188408 -
Record 158 of
Title:Structure design and analysis of circle wheel angle fine-tuning mechanism
Author Full Names:Jiang, Bo(1); Zhou, Shun(2); Guo, Yifan(2); Dong, Yiming(1)Source Title:Journal of Physics: Conference SeriesLanguage:EnglishDocument Type:Conference article (CA)Conference Title:2023 6th World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2023Conference Date:November 17, 2024 - November 19, 2024Conference Location:Hybrid, Wuhan, ChinaAbstract:In this paper, an angle fine-tuning mechanism for a monochromator is designed. Through finite element analysis, three kinds of flexure hinges are simulated and analyzed respectively, which are bow, chamfered straight beam, and oval. The results show that the chamfered straight beam hinge is the optimal design. The test results of the prototype show that the resolution of the designed angle fine-tuning mechanism can reach 0.1 arcsec and the repetition accuracy is less than 0.441 arcsec. All the indexes meet the needs of the monochromator. Therefore, the angle fine-tuning structure meets the requirements of sub-micro radian motion. ? Published under licence by IOP Publishing Ltd.Affiliations:(1) Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China; (2) School of Optoelectronics Engineering, Xi'An Technological University, Xi'an, ChinaPublication Year:2024Volume:2862Issue:1Article Number:012013DOI Link:10.1088/1742-6596/2862/1/012013數(shù)據(jù)庫ID（收錄號(hào)）:20244417289128 -
Record 159 of
Title:Compressed Spectrum Reconstruction Method Based on Coding Feature Vector Enhancement
Author Full Names:Cao, Chipeng(1,2); Li, Jie(3); Wang, Pan(1); Qi, Chun(3)Source Title:IEEE Transactions on Geoscience and Remote SensingLanguage:EnglishDocument Type:Journal article (JA)Abstract:Compressive spectral imaging (CSI) is a snapshot spectral imaging technique that rapidly captures the spectral information of a target in a single exposure and effectively reconstructs high spectral data using reconstruction algorithms. However, due to the presence of a large number of identical pixels in the measured image, which map to different prior spectral information, existing algorithms struggle to establish an accurate pixel separation representation model. To improve the separation effect between pixels and enhance the representation capability of the measured image pixels, we propose a compressed spectral reconstruction method with enhanced encoding feature vectors. By designing encoding information calculation rules based on a combination of linear and nonlinear functions, encoding features are calculated according to the spatial coordinate position information and wavelength information of the pixels, effectively enhancing the separation representation characteristics between channels and neighboring pixels through the addition of encoding features. Furthermore, by utilizing the semantic similarity between the predicted results of the prior model and the prior spectral image, the reconstruction problem is transformed into a total variation (TV) minimization problem between the predicted results of the prior model and the reconstruction results, combined with the alternating direction method of multipliers (ADMMs) to achieve accurate pixel reconstruction. The experimental setup utilizes a dual-camera compressed spectral imaging (DCCHI) system, consisting of a dual-dispersion coded aperture compressed spectral imaging (DD-CASSI) system and a grayscale imaging system. Various experiments have shown that the proposed method outperforms in reconstructing quality and displays superior algorithmic performance. ? 1980-2012 IEEE.Affiliations:(1) Xi'An Jiaotong University, School of Information and Communication Engineering, Shaanxi, Xi'an; 710049, China; (2) University of Chinese Academy of Sciences, Xi'An Institute of Optics and Precision Mechanics, Shaanxi, Xi'an; 710049, China; (3) Xi'An Jiaotong University, School of Information and Communications Engineering, Xi'an; 710049, ChinaPublication Year:2024Volume:62Start Page:1-16Article Number:5503016DOI Link:10.1109/TGRS.2023.3347220數(shù)據(jù)庫ID（收錄號(hào)）:20240215337320 -
Record 160 of
Title:Multi-spectral radiation thermometry of space point targets based on spectral image pixel binning
Author Full Names:Dong, Pengkai(1,2,3); Zhou, Liang(1,3); Liu, Zhaohui(1,3); Cui, Kai(1,3)Source Title:Applied OpticsLanguage:EnglishDocument Type:Journal article (JA)Abstract:The temperature characteristics of space point targets are essential indicators of their operational status and performance. To address the issue of significant temperature measurement errors in space point targets caused by low temperatures and a low imaging signal-to-noise ratio (SNR), we propose a mathematical model for multi-spectral radiation thermometry, derived from the principles of dual-band radiation thermometry. Furthermore, a multi-spectral image pixel binning method is introduced to enhance the SNR and minimize measurement errors. The experimental results indicate that the proposed multi-spectral radiation thermometry outperforms dual-band radiation thermometry. After merging 2 to 20 pixels, multi-spectral radiation thermometry in the 3.75–4.1 and 4.3–4.62 μm bands demonstrates an enhanced SNR and reduced temperature measurement errors. For a 378.15 K blackbody, the relative errors decrease from 1.52% and 2.19% to 0.26% and 0.74%, respectively, after merging six and eight pixels in the two different bands, compared to unmerged images. This method provides a valuable reference for developing techniques to enhance the SNR and improve temperature measurement accuracy for space point targets. ? 2024 Optica Publishing Group.Affiliations:(1) Xi’an Institute Optics and Precision Mechanics, Chinese Academy of Sciences, No. 17 Xinxi Road, Xi’an; 710119, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Key Laboratory of Space Precision Measurement Technology, Chinese Academy of Sciences, No. l7 Xinxi Road, Xi’an; 710119, ChinaPublication Year:2024Volume:63Issue:30Start Page:7900-7908DOI Link:10.1364/AO.537027數(shù)據(jù)庫ID（收錄號(hào)）:20244417296996 -
Record 161 of
Title:NVPCA Image Enhancement-Based Detection Method for Sidelobe Peak Parameters in Weak Signal Regions
Author Full Names:Wang, Zhengzhou(1); Wang, Li(1); Duan, Yaxuan(1); Li, Gang(1); Wei, Jitong(1)Source Title:Zhongguo Jiguang/Chinese Journal of LasersLanguage:ChineseDocument Type:Journal article (JA)Abstract:Objective The primary application of the host device involves research in high-energy density physics and inertial confinement fusion, handling energies up to 100000 joules. A significant challenge encountered during these experiments is the simultaneous detection of strong and weak signals in the far-field focal spot. Specifically, accurately measuring weak signals in the sidelobe area of the far-field focal spot has proven difficult. To address this, we introduce a peak parameter detection method for weak signal regions in the sidelobe, leveraging neighborhood vector principal component analysis (NVPCA) for image enhancement. Methods Our optimization strategy includes several steps. First, we treat each pixel in the sidelobe image and its eight neighboring pixels as a column vector to construct a 9-dimensional data cube. The first dimension post-PCA transformation, the NVPCA image, is then selected. Next, we employ angle transformation to detect various peak parameters of the one-dimensional sidelobe curve in all directions, facilitating the quantification of energy distribution in the sidelobe’s weak signal area. Subsequently, we identify the maximum position points of each sidelobe peak in all directions, linking these to form a maximum ring for each peak and calculating the grayscale mean of these rings. The smallest grayscale mean exceeding the LCM target separation threshold is identified as the minimum measurable signal for the entire sidelobe beam. Results and Discussions 1) We propose a sidelobe weak signal detection method using NVPCA image enhancement. This approach successfully isolates and extracts the minimum measurable signal from the 5th peak ring on the sidelobe image’s periphery, increasing the dynamic range ratio to 1.528 times. This method enhances the peak’s maximum value in any direction, ensuring the extraction of the minimum measurable signal from the peripheral 5th peak loop. 2) The LCM target detection threshold formula is employed to segregate the minimum measurable signal. This formula, tailored to the characteristics of far-field focal lobe images, effectively separates background noise. 3) We validate the one-dimensional curve peak parameters in various directions using a two-dimensional plane display method. Combining two-dimensional and one-dimensional displays, this method not only showcases the peak parameter distribution of one-dimensional sidelobe curves from multiple perspectives but also differentiates adjacent sampling angles’peak positions. The validation using equations (11) – (13) yields rising edge, falling edge, and pulse width consistent with those in Table 5, confirming the two-dimensional display method’s efficacy in verifying one-dimensional curve peak parameters. Conclusions Addressing the challenge of extracting the smallest measurable signal in the sidelobe image’s periphery for strong laser far-field focal spot measurements, we introduce a sidelobe weak signal region peak parameter detection method based on NVPCA image enhancement. Our findings demonstrate this method’s capability to isolate and extract the minimum measurable signal from sidelobe image peripheral peaks, increasing the dynamic range ratio to 1.528 times. This approach is crucial for accurately measuring weak signal areas in sidelobe beams, understanding their energy distribution, and laying the groundwork for future precise measurements of strong laser far-field focal spots in large-scale laser devices. ? 2024 Science Press. All rights reserved.Affiliations:(1) Laboratory Advanced Optical Instrument, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Science, Shaanxi, Xi’an; 710119, ChinaPublication Year:2024Volume:51Issue:6Article Number:0604003DOI Link:10.3788/CJL231185數(shù)據(jù)庫ID（收錄號(hào)）:20241215768417 -
Record 162 of
Title:Analysis of Bee Population and the Relationship with Time
Author Full Names:Li, Muyang(1); Liu, Xiaole(1); Qi, Chen(1); Liu, Lexuan(1); Yang, Kai(2,3)Source Title:Signals and Communication TechnologyLanguage:EnglishDocument Type:Book chapter (CH)Abstract:This essay proposes two methods to analyze bee populations in a given period. The first method is a quantitative analysis of the correlation between time and population, establishing a time–population model for bees. However, this method fails to provide a precise enough result. For improvement, the analysis of bee populations is augmented with more comprehensive factors (both positive and negative), creating a unified measure to calculate the total change in population percentage by assigning weights to each individual factor. During the construction of these two methods, we completed the following five steps: Find relevant data with a numerical correlation between time and population: Data containing relevant information like time and population were downloaded from credible sources. Then, the data were fitted with linear regression to reveal the relationship between the population and time. Find possible factors that affect bee populations: External and internal factors were identified through a literature review of research articles and reputable online sources. Among these, five factors were deemed the most critical and to be used in this chapter later. Assign weights to each factor through the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP): With EWM or AHP, a different set of weights was assigned to the factors. However, in this paper, neither of these two was used alone. Instead, a unified model that learns from both methods and hence generates a better weight for each factor is proposed and explained. Analysis of beehives needed to pollinate a 20-acre area: Parameters for the model were identified, defined, and populated using relevant data. Finally, the minimum and the maximum number of beehives that satisfy the requirements were calculated and an average of the values was obtained. Testing of the model on Buhlmann 1985: With the fully calculated weights of different factors through the integrated method, the model was tested to see if the weight assignments were reasonable. To do this, the result obtained from this model is compared with data approached by Buhlmann (1985) as an evaluation of this model. ? 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.Affiliations:(1) Amazingx Academy, Foshan, China; (2) Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China; (3) Xian Institute of Optics and Precision Mechanics of CAS, Xian, ChinaPublication Year:2024Volume:Part F2203Start Page:107-116DOI Link:10.1007/978-3-031-47100-1_10數(shù)據(jù)庫ID（收錄號(hào)）:20240515465518 -
Record 163 of
Title:Prediction of Bee Population and Number of Beehives Required for Pollination of a 20-Acre Parcel Crop
Author Full Names:Jin, Yukun(1); Wei, Tianyi(1); Shi, Jingru(1); Chen, Tingwen(1); Yang, Kai(2,3)Source Title:Signals and Communication TechnologyLanguage:EnglishDocument Type:Book chapter (CH)Abstract:The decline of the bee population poses threats to the production of considerable types of crops that require pollination. The prediction of the bee’s future population has therefore become a valuable research topic. For Problem one, we tried to solve it in mainly two ways: using the Grey Forecast Model and using differential equations. For data that were missing, we processed them by normalization at first and then regressed to find the abnormal data, and filled the missing data with average data after deleting abnormal data. For the Grey forecast, we use three types of models and compared their respective results with true values to pick the one with the most accurate output and use it to predict the population of bees. For the differential equation method, we simply express the rate of increase in population in terms of several variables (in the differential equation) and solve the equation to obtain the future population. For Problem two, we do a sensitivity test on the bee population. We applied the Random Forest model here to determine the importance of each variable. During the evaluation of the model, we test four sets of data and compare the Random Forest results with the true value. It turned out to be that the final model predicts the population precisely, which has proven that it is reliable. At last, we change the sensitivity of each variable for a 100% change and tell the importance of the variables. For Problem three, we get the model of the possibility of a plant being visited by a bee in a beehive system at any distance, and then we use this matrix to simulate the area and calculate the possibility at any point. After determining a possible lower bound, we can get the area that can reach the bound which is the area the current beehive system can serve. By changing the number and the positions of beehives, we can get the maximum area the system can serve at any time. We can also calculate the possibility considering the planting density and the population of bees so it can be related to problem 1. ? 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.Affiliations:(1) Amazingx Academy, Foshan, China; (2) Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China; (3) Xian Institute of Optics and Precision Mechanics of CAS, Xian, ChinaPublication Year:2024Volume:Part F2203Start Page:127-138DOI Link:10.1007/978-3-031-47100-1_12數(shù)據(jù)庫ID（收錄號(hào)）:20240515465509 -
Record 164 of
Title:Constructing 1D/0D Sb2S3/Cd0.6Zn0.4S S-scheme heterojunction by vapor transport deposition and in-situ hydrothermal strategy towards photoelectrochemical water splitting
Author Full Names:Liu, Dekang(1); Jin, Wei(1); Zhang, Liyuan(1); Li, Qiujie(1); Sun, Qian(1); Wang, Yishan(2); Hu, Xiaoyun(1); Miao, Hui(1)Source Title:Journal of Alloys and CompoundsLanguage:EnglishDocument Type:Journal article (JA)Abstract:Antimony sulfide (Sb2S3) is widely used in photocatalysts and photovoltaic cells because of its abundant reserves, low toxicity, environmental friendliness, narrow band gap, and high light absorption capacity. Sb2S3 shows a quasi-one-dimensional structure composed of [Sb4S6]n nanoribbons, a lot of reported studies are focused on preparing Sb2S3 with [hk1] oriented dominant growth to improve the photogenerated carrier transport capacity of Sb2S3. However, there is relatively few research on the preparation of [hk1] oriented rod-like Sb2S3 by vapor transport deposition (VTD) method. In this work, the VTD method was used to prepare Sb2S3 with [hk1] oriented growth on the FTO substrate, and then composite with the ternary solid solution CdxZn1?xS. Finally, a novel Sb2S3/Cd0.6Zn0.4S S-scheme heterojunction with rod-like core-shell structure was successfully constructed, which could effectively improve the photoelectrochemical properties. Because the solid solution component x is adjustable, that is, CdxZn1?xS has continuously adjustable band gap width and energy level position, the Sb2S3/CdxZn1?xS heterojunction type can be regulated from Type-II to S-scheme. Photoelectrochemical (PEC) tests indicated that the composite photoanode Sb2S3/Cd0.6Zn0.4S achieved a higher photocurrent density (2.54 mA·cm?2, 1.23 V vs. RHE), which is about 4.31 times that of pure Sb2S3 nanorod photoanode (0.59 mA·cm?2, 1.23 V vs. RHE). ? 2023 Elsevier B.V.Affiliations:(1) School of Physics, Northwest University, Xi'an; 710127, China; (2) State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences, Xi'an; 710119, ChinaPublication Year:2024Volume:975Article Number:172926DOI Link:10.1016/j.jallcom.2023.172926數(shù)據(jù)庫ID（收錄號(hào)）:20234915144994 -
Record 165 of
Title:Three-dimensional crumpled d-Ti3C2Tx/PANI structure enabled by PANI interlayer spacing control for enhanced electrochemical performance
Author Full Names:Zhao, Yuanbo(2); He, Weijun(2); Chen, Yanan(2); Liu, Yanan(2); Xing, Hongna(2); Zhu, Xiuhong(1,2); Feng, Juan(2); Liao, Chunyan(2); Zong, Yan(2); Li, Xinghua(2); Zheng, Xinliang(2)Source Title:Materials Today CommunicationsLanguage:EnglishDocument Type:Journal article (JA)Abstract:The self-stacking and collapsing of few-layered Ti3C2Tx(d-Ti3C2Tx) results in its poor rate capability and cycle performance during charge/discharge processes. Constructing a three-dementional (3D) structure, introducing interlayer spacers and using alkaline electrolytes are effective and powerful strategies to resolve the problems. Herein, a 3D crumpled d-Ti3C2Tx/PANI composite was successfully prepared by HCl/LiF in-situ etching Ti3AlC2 to obtain d-Ti3C2Tx and polymerizing PANI onto its surface with ice-bath stirring. Benefiting from the synergistic effect of kinetically favorable structure, component and alkaline electrolytes, The PM-1 (d-Ti3C2Tx/PANI-1) as an electrode remarkably improves the electrochemical performances compared with the original d-Ti3C2Tx in 2 M KOH electrolyte. It exhibits a specific capacitance of 230 mF cm?2（115 F g?1）at 2 mA cm?2, high rate capability of 81.2% at 20 mA cm?2 and outstanding stability of 96.7% retention after 5000 cycles at 10 mA cm?2. Furthermore, an assembled symmetric supercapacitor (SSC) also presents an excellent stability performance with 82.4% retention after 5000 cycles at 8 mA cm?2 and a promising energy storage performance. The related work provides a good reference for the MXene-based electrode materials in the conditions of alkaline electrolytes. ? 2024 Elsevier LtdAffiliations:(1) State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences, Xi'an; 710119, China; (2) School of Physics, Northwest University, Xi'an; 710069, ChinaPublication Year:2024Volume:39Article Number:108689DOI Link:10.1016/j.mtcomm.2024.108689數(shù)據(jù)庫ID（收錄號(hào)）:20241315799736 -
Record 166 of
Title:Location-Guided Dense Nested Attention Network for Infrared Small Target Detection
Author Full Names:Guo, Huinan(1,2); Zhang, Nengshuang(3); Zhang, Jing(3); Zhang, Wuxia(4); Sun, Congying(3)Source Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingLanguage:EnglishDocument Type:Journal article (JA)Abstract:Infrared small target (IST) detection involves identifying objects that occupy fewer than 81 pixels in a 256 × 256 image. Because the target is small and lacks texture, structure, and shape information on its surface, this task is highly challenging. CNN-based methods can extract rich features of the target. However, overly deep network structures may increase the risk of losing small targets. In addition, pixel-level positional deviations can also reduce the detection accuracy of IST. To address these challenges, we propose the location-guided dense nested attention network for IST detection. The proposed network consists of a pixel attention guided feature extraction module (PAG-FEM), a channel attention guided feature fusion module (CAG-FFM), and a detection module. First, the PAG-FEM utilizes the DNIM dense nested blocks from the DNANet as the backbone, integrating both channel and pixel attention mechanisms. This method focuses on the semantic and positional information of the targets, yielding semantic features that emphasize the positions of small targets. Second, the CAG-FFM employs upsampling and convolution operations to align the feature sizes, while utilizing the channel attention mechanism to obtain effective channel information. Then, these features are fused through stacking, addition, and averaging operations to obtain more discriminative features. Finally, the detection module uses eight-connected neighborhood clustering method to obtain the centroid coordinates of the targets for subsequent detection evaluation. Three datasets are utilized to verify our method, and experimental results show that our method performs better than other advanced methods. ? 2008-2012 IEEE.Affiliations:(1) Chinese Academy of Sciences, Xi'an Institute of Optics and Precision Mechanics, Xi'an; 710121, China; (2) Xi'an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi'an; 710121, China; (3) Xi'an University of Technology, Automation and Information Engineering, Xi'an; 710048, China; (4) Xi'an University of Posts and Telecommunications, Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an; 710121, ChinaPublication Year:2024Volume:17Start Page:18535-18548DOI Link:10.1109/JSTARS.2024.3472041數(shù)據(jù)庫ID（收錄號(hào)）:20244117175096 -
Record 167 of
Title:Denoising Algorithm based on Event Camera
Author Full Names:Lv, Yuanyuan(1,2); Liu, Zhaohui(1); Zhou, Liang(1); Qiao, Wenlong(1,2); Zhang, Haiyang(1,2)Source Title:Proceedings of SPIE - The International Society for Optical EngineeringLanguage:EnglishDocument Type:Conference article (CA)Conference Title:6th Conference on Frontiers in Optical Imaging and Technology: Novel Detector TechnologiesConference Date:October 22, 2023 - October 24, 2023Conference Location:Nanjing, ChinaConference Sponsor:The Chinese Society for Optical EngineeringAbstract:The event camera is a novel type of bio-inspired vision sensor inspired by the biological retina. Compared to traditional frame-based cameras, it offers high temporal resolution, high dynamic range, reduced redundancy, and lower transmission bandwidth. These unique features pave the way for innovative solutions in the field of computer vision. However, the heightened sensitivity of event cameras to fluctuations in brightness, along with their susceptibility to environmental factors and hardware limitations, presents a significant challenge. It involves capturing spatiotemporal information from the target signal simultaneously with the generation of a substantial volume of noise events. In applications relying on event cameras, this noise compromises target detection precision. Therefore, event stream denoising is essential before further applications can be pursued. Unfortunately, conventional frame-based algorithms are ill-suited for processing event data due to the distinct format of event cameras. In response to the challenges of event stream denoising, using the event stream generated by Celex-V as an example, this paper categorizes noise events and conducts an analysis of the event noise distribution model. Leveraging the characteristics of noise events, such as randomness and isolation, the paper proposes an event-based cascaded noise processing method. This method involves analyzing events in the spatiotemporal vicinity of arriving events and removing noise events from the event stream data. While ensuring the integrity of data flow information, it achieves rapid and efficient noise removal. The denoised event stream is advantageous for subsequent processing in various applications based on event cameras. ? 2024 SPIE.Affiliations:(1) Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an; 710119, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, ChinaPublication Year:2024Volume:13154Article Number:1315409DOI Link:10.1117/12.3016236數(shù)據(jù)庫ID（收錄號(hào)）:20242016095187 -
Record 168 of
Title:A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n
Author Full Names:Wang, Jiale(1,2); Bai, Zhe(1); Zhang, Ximing(1); Qiu, Yuehong(1)Source Title:Remote SensingLanguage:EnglishDocument Type:Journal article (JA)Abstract:Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks. ? 2024 by the authors.Affiliations:(1) Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an; 710119, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, ChinaPublication Year:2024Volume:16Issue:5Article Number:857DOI Link:10.3390/rs16050857數(shù)據(jù)庫ID（收錄號(hào)）:20241115749023