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检索条件"任意字段=Signal and Data Processing of Small Targets 1995 Conference"

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

引用

2023 International conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Xue, Min Gao, Yuexin Wu, Zhixin Xidian University National Key Laboratory of Radar Signal Processing Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350318012

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data. © 2023 IEEE.

关键词： Compressed sensing

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Maritime Target Detection Method Based on SDP-YOLOv8

Maritime Target Detection Method Based on SDP-YOLOv8

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Xiao Luo Hao Ding Yunlong Dong Information Fusion Institute Naval Aviation University Yantai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Due to the influence of sea clutter, detecting maritime targets, especially small ones, is extremely challenging. With the continuous development of image recognition technology, techniques that convert one-dimensional maritime radar echo data into two-dimensional images and use neural networks for target recognition have been proposed. This paper applies this technology by converting radar echo data containing targets and pure sea clutter into two-dimensional images using the Symmetric data Path (SDP) method. The YOLOv8 algorithm is then employed for classification to determine the presence of targets. The effectiveness of the proposed method is validated using a dataset shared by the Naval Aviation University.

关键词： Image recognition Target recognition Radar clutter Neural networks Object detection Radar imaging Sea state data processing Classification algorithms Clutter

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A method for enhancing infrared small target images under complex sky backgrounds

A method for enhancing infrared small target images under co...

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Yuhan Xu Linbo Tang Haotian Sun Beijing Key Laboratory of Embedded Real-Time Information Processing Radar Research LabSchool of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Infrared target detection is currently widely used in both military and civilian fields. Due to the lower resolution of infrared images compared to visible light images and the lack of texture information in small targets, the quality of infrared small target images is often poor and not suitable for direct detection. Therefore, enhancing low-quality infrared small target images is a necessary step before object detection can be performed. This article presents a method for enhancing infrared images of small targets. The process begins with applying guided filtering to the original image. The resulting detail layer is then used as a guided image for a second guided filtering of the base layer, producing a second detail layer. Additionally, a novel approach to local contrast enhancement is introduced, which applies contrast enhancement specifically to this secondary detail layer. Experimental results has been demonstrated that the enhancement method proposed in this paper significantly improves the visibility of the target area while reducing background interference, without losing information about small targets. Additionally, this method outperforms other common techniques in terms of enhancement effect, as shown in comparative experiments.

关键词： Image quality Image resolution Object detection Interference Information filters data processing Image enhancement

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Research on data processing method for detection of small and weak targets in space

Research on data processing method for detection of small an...

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conference on AOPC - Optical Sensing, Imaging, and Display Technology

作者： Sen, Tian Yan, Wen Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781510661905;9781510661912

Space debris identification and localization has been studied for a long time at home and abroad, but there are still shortcomings in the detection of very low SNR small targets, especially the detection and recognition of very low SNR faint small targets submerged in the dense galactic background Stars in order to effectively target detection, background removal is essential, in order to distinguish between the target and the stars, through the accumulation of judgement block number of star closely adjacent to judge, if the judgment of the block, star closely adjacent number more than set threshold, so that the point for stars, on the other hand, argue that point as the goal to be detected, then, to the star point of the block The results of the algorithm show that, according to different star map images, the size of matrix block and the threshold of star point can be modified to achieve better star removal effect.

关键词： the Milky Way's background low signal to noise ratio target detection multiframe superposition

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A small Target Detection Method in Sea Clutter Based on Feature Manifold

A Small Target Detection Method in Sea Clutter Based on Feat...

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Jian Guan Xingyu Jiang Baoxin Chen Hao Ding Yunlong Dong Ningbo Liu Information Fusion Institute Naval Aviation University Yantai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Target detection methods based on multidimensional features are often applied to the detection of small targets in sea clutter. However, existing algorithms do not fully utilize the correlation information between features, which to some extent limits their performance. To address this issue, this paper proposes a small target detection method in sea clutter using feature manifolds. By establishing a high-dimensional manifold representation of radar echo feature data, the radar target detection problem is transformed into a discrimination problem between two geometric points on a feature matrix manifold. This method integrates the use of multidimensional features and their correlation information, thereby effectively improving target detection performance. Experimental results on the SDRDSP dataset demonstrate that the proposed detection approach outperforms existing feature-based methods in terms of detection accuracy.

关键词： Manifolds Correlation Radar clutter Radar detection Object detection Feature extraction data processing Radar signal processing Clutter Testing

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Aerial Photovoltaic Panel Infrared Image Defect Detection Method Based on Improved YOLOv8

Aerial Photovoltaic Panel Infrared Image Defect Detection Me...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Hongtao Hu Yunxiang Li Junfeng Wang Yong Lu School of Computer Science Xi'an ShiYou University Xi'an China No.3 Oil Production Plant PetroChina Changqing Oilfield Company Xi'an Shannxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Photovoltaic panels are the core equipment of photovoltaic power generation. Defects in photovoltaic panels are generally detected by analyzing infrared images taken by drones. However, the photovoltaic panel defects to be detected in infrared images are small, and traditional target detection algorithms are not sensitive to small targets. Misdetections and missed detections are prone to occur. In response to the above problems, this article proposes an Improved-YOLOv8 model to increase the accuracy of small target detection. Firstly, the CA attention mechanism is integrated into the YOLOv8 model to improve the ability to extract defect target features and locations; secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to replace Concat to reduce the loss of target information and enhance the model's ability to fuse targets of different scales. Maintain the sensitivity of the model to targets of different scales; finally, experiments were conducted using infrared image data of photovoltaic panels taken aerially from a photovoltaic power station. The results show that the accuracy and mAP@0.5 of the Improved-YOLOv8 model reached 88.5% and 95.2% respectively, effectively improving the YOLOv8 model's ability to handle small target defects in photovoltaic panels.

关键词： Photovoltaic systems Accuracy Attention mechanisms signal processing algorithms Object detection signal processing Feature extraction data models Drones Defect detection

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Local Information Guided Global Integration for Infrared small Target Detection

Local Information Guided Global Integration for Infrared Sma...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qiang Li Qianchen Mao Wenjie Liu Jinbao Wang Wenmin Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Faculty of Innovation Engineering Macau University of Science and Technology Macau China

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

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A Reinforcement Learning Agent Controlled Multi-branch small Object Detection Framework

A Reinforcement Learning Agent Controlled Multi-branch Small...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Junkun Hong Yitian Long Yueyi Luo Liujie Hua Jun Long Qianqian Qi School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential small object region. However, these solutions inevitably add sophisticated detectors and extra learning components, which incur time-consuming and computation-costing. Meanwhile, we observe that it’s suboptimal to extract fine features in such a generic way. To alleviate the issues, we propose a multi-branch small object detection framework with a regular-scale detection branch and a small-scale detection branch. Specifically, we design and pre-train a reinforcement learning agent to control feature extractors in both branches according to the results of small object areas. Moreover, we present a region clipping algorithm to rebuild the small object to regular size, which can be input into a mature detector directly. The extensive experiments on COCO, VisDrone, SODA-D, and our collecting TVDS datasets demonstrate our method outperforms the state-of-the-art methods in several metrics.

关键词： Measurement Pipelines signal processing algorithms Object detection Reinforcement learning Detectors signal processing Feature extraction data mining Speech processing

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD)

Infrared small target detection based on long-short term fra...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Bing Zhu Jiahui Shi Ruixing Yu Weixin Gao Siyu Wang School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. The algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. Then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. The experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data.

关键词： Accuracy signal processing algorithms Object detection Cameras Motion compensation Trajectory Image sequences Motion measurement Time-domain analysis signal to noise ratio

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A Cascade-filtered Adaptive CFAR Detector in Low small Target Detecting

A Cascade-filtered Adaptive CFAR Detector in Low Small Targe...

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signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Leibo Ni Wenwen Xu Hao Wang Fengze Zhang Dalong Xu Jianyin Cao Nanjing University of Science and Technology Nanjing China Nanhu Laboratory Jiaxing China Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the context of detecting low-flying small targets in urban high-rise environments, the targets are often obscured by large clutter reflectors or non-uniform clutter regions. This paper proposes a cascaded filtering adaptive constant false alarm rate (CFAR) detector. This method consists of two cascaded detectors. Units judged as "target present" by the first stage are filtered out in the second stage. Compared to single-stage detectors, this method effectively integrates the performance advantages of multiple CFAR techniques. This makes it suitable for more complex detection environments. Based on measured data and Monte Carlo simulation results, the cascaded filtering CFAR detector has a high detection rate in multi-target environments and a low false alarm rate in non-uniform clutter edge environments.

关键词： Monte Carlo methods Fluctuations Filtering Radar measurements Image edge detection Simulation Detectors data processing Clutter

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