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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Fu, Luhua Hu, Yutong Pang, Jiaming Dong, Liu Shuai, Jia Ma, Baoqiang Key Laboratory of Photoelectric Control Technology Tianjin300072 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China Tianjin University Tianjin300072 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

This paper studies a method of detecting the position of crankshaft flange hole group based on vision measurement, and sets up the position detection system of crankshaft flange hole group. The relative measurement model of hole group position is established by using the standard crankshaft information, and the system calibration method is studied. In this paper, a multi-camera calibration method based on polynomial fitting two-dimensional image mapping model is adopted. In addition, the image processing technology of hole group is studied. The improved Canny edge detection method is used to extract the contour of the hole group. Redundant edge filtering algorithm is used to eliminate unreliable edges. Then use the gradient interpolation method to extract sub-pixel edges. The measurement results show that the single-direction measurement error of the central coordinate of the crankshaft flange hole group is less than 0.07mm, and the repeatability error is less than 0.009mm, which provides a basis for the realization of industrial online efficient detection. © 2022 SPIE

关键词： Calibration

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Optimization of optical convolution kernel of optoelectronic hybrid convolution neural network

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Optoelectronics Letters 2022年第3期18卷 181-186页

作者： XU Xiaofeng ZHU Lianqing ZHUANG Wei ZHANG Dongliang LU Lidan YUAN Pei School of Electro-Optical Engineering Changchun University of Science and TechnologyJilin 130022China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology UniversityBeijing 100192China Beijing Laboratory of Optical Fiber Sensing and System Beijing Information Science and Technology UniversityBeijing 100016China

To enhance the optical computation’s utilization efficiency, we develop an optimization method for optical convolution kernel in the optoelectronic hybrid convolution neural network(OHCNN). To comply with the actual calculation process, the convolution kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture’s accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution kernel. The energy efficiency ratio(EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.

关键词： convolution kernel weights

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Infrared Target Detection Based on the Fusion of Mask R-CNN and Image Enhancement Network

Infrared Target Detection Based on the Fusion of Mask R-CNN ...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Fang Guan, Song Yu, Kuaikuai Gong, Hua Shengyang Ligong University College of Science Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing300308 China

ISBN: (纸本)9781665465335

In this paper, we focus on the problem of the infrared image detection. Aiming at the problem of the low contrast of the infrared images, an adaptive gamma transformation algorithm (AGT) is established to intelligently improve the contrast of the infrared images. Aiming at the problem of the weak edge information of the infrared images, an improved Sobel operator edge enhancement algorithm (ISOEE) is designed to enrich the edge features of the infrared images. In order to solve the problem that the traditional infrared image detection algorithm relies on expert experience, the fusion infrared images detection model AIE-Mask R-CNN based on Mask R-CNN, AGT algorithm and ISOEE algorithm is proposed. This fusion model can realize accurate and intelligent detection for the infrared images. The experimental results show that the fusion model AIE-Mask R-CNN improves the detection accuracy of the infrared images. © 2022 IEEE.

关键词： Edge detection

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Multi-view infrared small target recognition based on YOLOv5

Multi-view infrared small target recognition based on YOLOv5

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2022 Chinese Automation Congress, CAC 2022

作者： Gong, Hua Liu, Shasha Guan, Song Yu, Kuaikuai Liu, Fang College of Science Shengyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing300308 China

ISBN: (纸本)9781665465335

In order to improve the recognition accuracy of small infrared targets in complex environment, a SyluInfrared dataset is built by taking multi-view infrared images with the Frock TiX600 infrared thermal imager. Seven image enhancement algorithms including image brightening, contrast adjustment, image blurring, image flipping and image scaling are designed to improve the image quality of small infrared targets. The YOLOv5 deep learning model is used to identify small infrared targets with multiple views to improve the recognition accuracy of small infrared targets. The experimental results show that the accuracy of multi-view infrared small target recognition model for YOLOv5 deep network is 97.7%, which is 6.6% higher than that of single-view infrared small target recognition model based on YOLOv5. The multi-view target recognition strategy significantly improves the infrared small target recognition performance. © 2022 IEEE.

关键词： Image enhancement

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Rotational random extraction of Toeplitz matrix for ghost imaging 4

Rotational random extraction of Toeplitz matrix for ghost im...

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4th International Conference on Optoelectronic science and Materials, ICOSM 2022

作者： Cheng, Zhengdong Sun, Maosheng Chen, Yi Chang, Haikuan Liu, Yanyan State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Huangshan Road No.460 HeFei230037 China Science and Technology on Electro-Optical Information Security Control Laboratory Weiwu Road No.9 Tianjin300450 China

ISBN: (纸本)9781510662209

If Toeplitz matrix is used for compression-aware ghost imaging, the imaging quality will be very low. In order to solve the problem, a new experimental scheme for ghost imaging is proposed in this paper. The scheme first extracts the Toeplitz matrix elements randomly and sparsely using a revolving matrix, and then modulates the illumination light field. The sampling is performed by using the Toeplitz matrix as a fixed matrix and another measurement matrix as a revolving matrix. The revolving matrix rotates around its center at a constant angular velocity. The fixed Toeplitz matrix is superimposed with the revolving matrix to form the optical field modulation matrix. The results of simulation experiments show that light field modulation scheme of the rotational random extraction of Toeplitz matrix can modulate the light field with more randomness. The use of this scheme in ghost imaging experiments results in high quality images with low distortion. © 2023 SPIE. All rights reserved.

关键词： Matrix algebra

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Specification and counterexample generation for cyber-physical systems

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Soft Computing 2024年第17-18期28卷 9137-9155页

作者： Li, Zhen Cao, Zining Wang, Fujun Xing, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Science and Technology on Electro-optic Control Laboratory Henan Luoyang471000 China Collaborative Innovation Center of Novel Software Technology and Industrializaton Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210023 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Jiangsu Nanjing211106 China

Cyber-Physical Systems (CPS) are complex systems that integrate information control devices with physical resources, which can be automatically and formalized verified by model checking according to the expected requirements in the formal specification. The counterexamples in model checking are witnesses to the violation of the specification properties of the system and can provide important diagnostic information for debugging, controlling, and synthesizing CPS. Designing a rational specification language for CPS and generating effective counterexamples allows security vulnerabilities to be detected and addressed early in the system development. However, CPS involve frequent interactions between cyber and physical systems and often operate in unreliable environments, which poses new challenges for comprehensive modeling and designing specification languages for CPSs with discrete, continuous, time, probabilistic, and concurrent behaviors. Moreover, finding the smallest counterexample of CPS with probabilistic behavior in the shortest possible time has been identified as a Non-Deterministic Polynomial-complete (NP-complete) problem. Although a number of heuristics have been devised to address this challenge, the accuracy and efficiency of the solved counterexamples need to be improved due to the difficulty in determining the heuristic functions. We first provide a comprehensive model for CPS by introducing the Hybrid Probabilistic Time Labeled Transition System (HPTLTS). Subsequently, we design a specification language HPTLTS Temporal Logic (HPTLTS-TL) that can describe the properties of CPS. In addition, we propose an optimization algorithm CACO-A, which combines the Ant Colony Optimization (ACO) algorithm and the A-algorithm to efficiently generate the counterexample of CPS, which is represented as the diagnostic subgraph. Finally, we discuss a typical CPS example to demonstrate the feasibility of our approach. © The Author(s), under exclusive licence to Springer-

关键词： Embedded systems

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System 21

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Zhou, Yinnan Zhou, Hao Li, Luowei Chen, Siming Liu, Zhi Li, Xiang-Yang LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn’t be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions. © 2024 IEEE.

关键词： Wi-Fi

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Multi-scale network combined with multi-loss optimization for low-light image super-resolution reconstruction 5

Multi-scale network combined with multi-loss optimization fo...

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5th International Conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Liu, Bofeng Yu, Haiyang Hu, Xiaojuan Li, Yanfeng School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology Ministry of Education Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9781510682863

The proposed super resolution reconstruction method aims to address the issues of missing detailed information in low light environments and poor recovery of high frequency details in reconstructed images. To fully extract the retained features of low-light images, a multi-scale feature fusion learning network is introduced, which utilizes convolution to extract shallow features and then extracts deep features through multiple cascaded multi-scale feature extraction modules. Additionally, an attention mechanism is incorporated to adaptively learn the importance of each channel and finally fuse the obtained shallow and multi-scale features. Furthermore, perceptual loss and edge loss are integrated into the loss function to optimize the network for high frequency texture details in the recovered image. Experimental results demonstrate that this method yields clearer edge structures and texture details after super-resolution reconstruction of images acquired in low light environments, outperforming other existing methods both visually and objectively. © 2024 SPIE.

关键词： Convolutional neural networks

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Ship Size Attribute Reconstruction Method Based on Multi-aircraft Collaborative Detection 7th

Ship Size Attribute Reconstruction Method Based on Multi-air...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xiaozu, Li Qingxi, Chi Peng, Jiang Mengmeng, Han Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9789819733316

For mixed-ship scenarios, traditional aircraft have limited ability to extract ship features due to the constraints of observation angles, making it difficult to effectively select the high-value ship targets. In this paper, a ship length and width reconstruction method adaptable to multi-aircraft cooperative detection is proposed, taking dual-aircraft radar cooperative detection as the research object. Under the premise of known heading, a ship size reconstruction model is established by combining the radial size information from different observation directions. Finally, the performance of the model under different detection conditions is analyzed. Simulation verification shows that the maximum length reconstruction error does not exceed 7.56% of the true length of the target, which can effectively restore the target size information and provide necessary support for effectively solving the problem of the high-value ship targets selection in mixed scenes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ships

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Optimal matching based on singular value decomposition for star pattern recognition 8

Optimal matching based on singular value decomposition for s...

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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Liang, Jiang Shijie, An Yalei, Qi Shuizhong, Chen Shengwei, Zhang Science and Technology on Electro-Optical Control Laboratory Luoyang471009 China Luoyang Institute of Electro-Optical Equipment AVIC Luoyang471009 China Shenyang Aircraft Design and Research Institute AVIC Shenyang11000 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

For the purpose of improving the recognition rate and the anti-noise ability, a new star pattern recognition algorithm is proposed, which is based on optimal singular value decomposition matching. The algorithm can effectively solve the recognition failure caused by redundant matching results, which the triangle recognition algorithm and the pyramid algorithm can not. Compared with traditional star pattern recognition algorithms, the recognition rate and the anti-noise ability of the new algorithm proposed in this paper are improved. With the star position noise added (Gauss noise, mean=0, s=3pixel), the recognition rate is 98.91%, and the average recognition time assumption is 20.21ms. © 2022 SPIE

关键词： Singular value decomposition

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