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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

共 865 条记录，以下是81-90 订阅

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Dual-Level Contrast Enhancement Method for Infrared small Target Detection

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

引用

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

作者： Haotian Sun Wenzheng Wang Yuhan Xu Qiuyu Jin Zipeng Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds; however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target’s SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target’s radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results.

关键词： Filters Simulation Object detection Interference Reconnaissance Robustness Windows Noise measurement signal to noise ratio Visual perception

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Fast small Object Detection Algorithm Based on Feature Enhancement and Reconstruction 13

Fast Small Object Detection Algorithm Based on Feature Enhan...

引用

13th International conference on Wireless Communications and signal processing, WCSP 2021

作者： Huo, Zhiyong Yan, Tianwen Cao, Weiye Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. The paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. The algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4. © 2021 IEEE.

关键词： Convolution

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DETECTION OF small MOVING GROUND VEHICLES IN CLUTTERED TERRAIN USING INFRARED VIDEO IMAGERY

DETECTION OF SMALL MOVING GROUND VEHICLES IN CLUTTERED TERRA...

引用

IEEE International conference on Image processing (ICIP)

作者： Cuellar, Adam Mahalanobis, Abhijit Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665441155

The detection of small moving targets in cluttered infrared imagery remains a difficult and challenging task. Conventional image subtraction techniques with frame-to-frame registration yield very high false alarm rates. Furthermore, state of the art deep convolutional neural networks (DCNNs) such as YOLO and Mask R-CNN also do not work well for this application. We show however, that it is possible to train a CNN to detect moving targets in a stack of stabilized images by maximizing a target to clutter ratio (TCR) metric. This metric has been previously used for detecting relatively large stationary targets in single images, but not for the purposes of finding small moving targets using multiple frames. Referred to as moving target indicator network (MTINet), the proposed network does not rely on image subtraction, but instead uses depth-wise convolution to learn inter-frame temporal dependencies. We compare the performance of the MTINet to state of the art DCNNs and a statistical anomaly detection algorithm, and propose a combined approach that offers the benefits of both data-driven learning and statistical analysis.

关键词： Detection Localization Infrared CNN

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Multiband Radar signal Fusion and Extrapolation Method Based on Transformer Model

Multiband Radar Signal Fusion and Extrapolation Method Based...

引用

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

作者： Chunxu Gong Weidong Li Ruiming Lu Rui Wang Radar Technology Research Institute Beijing Institute of Technology Beijng China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The improvement of range resolution is crucial for enhancing the capability of imaging radars to detect small targets, such as drones. However, due to technical and cost limitations, current radars cannot indefinitely increase hardware bandwidth to enhance range resolution. To address this challenge, the multiband fusion and extrapolation technique has emerged. This method uses multiple discrete sub-band radar signals and fuses them to extrapolate the missing frequency bands, effectively achieving a wideband radar signal. Traditional approaches primarily rely on constructing signal models and estimating model parameters to accomplish fusion and extrapolation. This paper is the first to apply machine learning techniques to the field of multiband fusion and extrapolation, proposing a radar multiband signal fusion and extrapolation method based on a Transformer model. By training the Transformer network, the method enables accurate estimation of unknown frequency band signals. The effectiveness of the approach is validated through electromagnetic simulation data of drone echoes, demonstrating significantly improved estimation accuracy compared to traditional signal fusion and extrapolation methods.

关键词： Extrapolation Accuracy Image resolution Estimation Radar Machine learning Radar imaging Transformers Frequency estimation Electromagnetics

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Multi-view Feature Discrepancy Attack for Single Object Tracking

Multi-view Feature Discrepancy Attack for Single Object Trac...

引用

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

作者： Zhiheng Li Weng Zhimin Yuehuan Wang Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adversarial attacks on single object tracking (SOT) have attracted increasing attention. However, most previous works have focused on adding small digital perturbations to tracking sequences, assuming access to the data, which makes these attacks impractical in real-world applications. Inspired by traditional military camouflage, we propose a texture pattern attack method with a similar implementation, called the Multi-View Feature Discrepancy Attack (MFDA). Unlike the consistent texture features of traditional camouflage, we iteratively optimize non-planar textures by obtaining gradients from the target model to enhance feature discrepancies across different viewpoints. Moreover, the model’s multi-scale features and heatmaps are utilized as targets for our feature-level and decision-level attacks, respectively. We apply texture patterns to controllable regions of vehicle models and conduct extensive attack experiments. The results show that our method significantly degrades the performance of state-of-the-art Siamese-based trackers, and also exhibits attack capability against ViT-based trackers.

关键词： Heating systems Target tracking Perturbation methods signal processing Acoustics Surface texture Object tracking Speech processing Autonomous vehicles

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Bioradar signal processing Algorithms based on Machine Learning

Bioradar Signal Processing Algorithms based on Machine Learn...

引用

Integrated Intelligence and Communication Systems (ICIICS), International conference on

作者： Huanyi Guo Wuhan University of Bioengineering Wuhan Hubei China

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Since human physiological signals (e.g., respiration and heart rate) generate very small Doppler shifts, they are highly susceptible to being masked by strong clutter backgrounds. The main research focus of this project is to accurately extract weak targets (i.e., vital signs) from complex environments. The signals received by BioRadar contain vital signs signals and clutter signals. Based on the analysis of the collected data, it is known that vital signals have low signal-to-noise ratio and are characterized by quasi-periodicity and multiple harmonics; whereas clutter can be manifested as Gaussian colored noise. For this reason, this study translates the bio-radar signal processing into the problems of harmonic modeling, clutter suppression, and signal-to-noise ratio enhancement in the presence of Gaussian colored noise. The research objective is to extract the weak vital sign signals of human body from the complex background by machine learning techniques. In order to effectively extract physiological signals such as respiration and heart rate, this paper employ an ultra-wideband bio-radar system and advanced signal processing algorithms, and the experimental results show that the respiratory signal of participant 1 at 0 s is 2.1 mV, the heartbeat signal is 1.0 mV, and the suppressed clutter signal is 0.1 mV, which effectively suppresses the clutter interference with high accuracy and reliability.

关键词： Accuracy Buildings signal processing algorithms Machine learning Harmonic analysis Physiology Clutter Colored noise signal to noise ratio Ultra wideband technology

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On Joint Active-Passive Sonar Detection Performance of Underwater Long-Range Low-Speed small Target

On Joint Active-Passive Sonar Detection Performance of Under...

引用

IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Kai Lai Xionghou Liu Yixin Yang Chao Sun School of Marine Science and Technology Northwestern Polytechnical University Xi'an P. R. Chinal Shaanxi Key Laboratory of Underwater Information Technology Xi'an China P. R. China

We discuss the feasibility of the joint active-passive sonar detection method of underwater low-speed small targets located at long ranges. Specifically, the transmission loss, the reverberation level, and the noise level related to the joint active-passive detection method are modeled and calculated. The relationship between the figure of merit and the transmission loss is used to analyze the performance of the joint active-passive detection method of underwater low-speed small targets. For obtaining a long detection range, we mainly consider the working frequency of both the active and passive sonars to be much lower than traditional small target detection sonars. The active sonar is set to be worked at a frequency band not higher than 40kHz. Meanwhile, the passive sonar is supposed to work at a frequency band not higher than 1 kHz. The factors that affect the performance of the joint active-passive detection method are analyzed in detail. The results indicate that the considered joint active-passive detection method may have a detection range of more than 2000m in a relatively good underwater acoustic environment, which provides a potential way to significantly increase the working range of the underwater detection sonar system.

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small Target Detection on Sea Surface based on Feature-Weighted Support Vector Machine

Small Target Detection on Sea Surface based on Feature-Weigh...

引用

IEEE International conference on signal and Image processing (ICSIP)

作者： Yaqing Chen Shijian Shen Zhiyong Lei Nanjing Research Institute of Electronics Technology Nanjing China

Because the sea clutter is greatly affected by the complex and changeable environment, it is difficult to describe its characteristics through a single amplitude statistical model. In addition, the great variety and the small radar cross section of the target on the sea surface increase the detection difficulty. This paper proposes a feature-weighted SVM algorithm, which regards the detection of small targets on the sea surface as a binary classification problem. The algorithm extracts six features from different domains of the sea clutter, calculates the weight of the features according to the contribution of the features and constructs the feature space for detection with weighted feature vectors. Experiments on IPIX data sets show that the detection method improves the detection accuracy compared with the traditional algorithm and it is helpful for sea surface monitoring.

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An Intelligent Particle Filter Based Joint Probabilistic data Association Algorithm for Long-Duration Multi-Target Tracking in Clutter Environment

An Intelligent Particle Filter Based Joint Probabilistic Dat...

引用

IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Xihao Wang Die Chen Yanru Zhao School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Joint Probabilistic data Association Algorithm based on Particle Filter (JPDA-PF) is used in the tracking problem of nonlinear multi-target systems in dense clutter environments. However, during long time tracking, the tracking accuracy decreases with the increase of time. Meanwhile JPDA produces a combinatorial explosion in the number of associated events as the number of targets and clutter increases, greatly increasing the computational task. In this paper, a Joint Probabilistic data Association Algorithm based on dolphin swarm particle filter (JPDA-DSPF) is proposed. Using the dolphin swarm optimization algorithm as a resampling method for particle filter can optimize the particle distribution to be closer to the region of high likelihood, and enhance the particle diversity to weaken the particle degradation phenomenon. We use an adaptive sequential approach to reduce computational costs by discarding correlated events with small probabilities. Finally, it is proved by simulation experiments that the method can effectively reduce the tracking error, inhibit the increasing trend of the error with time, effectively reduce the computation task and enhance the timeliness during multi-target encounters.

关键词： Target tracking signal processing algorithms signal processing Probabilistic logic Market research Information filters Particle filters Clutter Particle swarm optimization Dolphins

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Point Cloud Based In-vehicle Occupancy Detection Method by Using 77GHz mmWave Radar

Point Cloud Based In-vehicle Occupancy Detection Method by U...

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

作者： Xinda Cao Jiangang Liu Yulin Wu Fengzhi Shao Yubo Wang Guolong Cui Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of China Quzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In recent years, with the introduction and development of vehicle-to-everything (V2X) and child presence detection (CPD), there’s an increasing demand for in-vehicle perception systems. Millimeter-wave (mmWave) radar has become one of the mainstream sensors in this field for high accuracy, small size, and low power consumption. In this paper, we develop a real-time in-vehicle occupancy detection method based on 77GHz mmWave radar. Firstly, a pre-processing framework based on dual-path detection is used to obtain more point clouds from weak targets. Then density-based spatial clustering of applications with noise (DBSCAN) is applied to classify the occupants’ point clouds. Finally, we propose a novel probabilistic population-assisted occupancy detection algorithm based on the long and short-term feedback results, which can suppress missed detection and false alarms well. Our algorithm has been successfully deployed onto the radar board and achieves an average accuracy rate of 99.10% under various complex scene experiments.

关键词： Point cloud compression Accuracy Radar detection signal processing algorithms Probabilistic logic Real-time systems Radar signal processing Sensors Millimeter wave communication Vehicle-to-everything

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