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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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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...

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

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

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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...

引用

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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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

引用

2nd International conference on Advanced Algorithms and signal Image processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： signal detection

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CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of Feature Information and Applying it to Substation Equipment Detection

CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of...

引用

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

作者： Li Zhuyun Xu Chong Yang Yingyi Lai Jiayang Xian Qu Mai Xiaoming Ieiri Yuya Yoshie Osamu China Southern Power Grid Technology Co. Ltd. Guangzhou China Graduate School of Information Production and Systems Waseda University Fukuoka Japan BIWIN Storage Technology Company Limited Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Substations act as bridges connecting power gener-ation with consumption and play crucial roles in electrical grids such as power energy conversion, operational dispatching, and information monitoring. With the rapid development of electrical grids in recent years, the increasing contradiction between the shortage of production staff and the growing workload of inspections has urgently necessitated intelligent recognition algorithms for the condition of indoor substation equipment. This paper proposes a novel indoor substation equipment target detection framework based on YOLOv8, aimed at addressing issues like scale variation, occlusion handling, and real-time performance optimization, thereby enhancing detection accuracy and speed in complex environments. By incorporating the ConvNeXt feature extraction network and BiFPN, combined with the Wise-IoU loss function and adding detection heads for small targets, this approach has improved the detection capabilities for multi-scale targets, particularly in recognizing minute objects. Experimental results with actual field data indicate that the proposed CWA-YOLO model outperforms existing methods in both detection accuracy and speed, with the model mAP0.5 and mAP0.5:0.95 being 0.918 and 0.623 respectively, marking improvements of 6.50% and 11.05% over the YOLOv81 model, 7.49% and 11.85% over YOLOv71, and 9.55% and 15.80% over YOLOv51.

关键词： Substations Accuracy Target recognition signal processing algorithms Object detection Production signal processing Feature extraction data models Real-time systems

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GASS: Generalizing Audio Source Separation with Large-Scale data

GASS: Generalizing Audio Source Separation with Large-Scale ...

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

作者： Jordi Pons Xiaoyu Liu Santiago Pascual Joan Serrà Dolby Laboratories

Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the potential of universal source separation is limited because most existing works focus on mixes with predominantly sound events, and small training datasets also limit its potential for supervised learning. Here, we study a single general audio source separation (GASS) model trained to separate speech, music, and sound events in a supervised fashion with a large-scale dataset. We assess GASS models on a diverse set of tasks. Our strong in-distribution results show the feasibility of GASS models, and the competitive out-of-distribution performance in sound event and speech separation shows its generalization abilities. Yet, it is challenging for GASS models to generalize for separating out-of-distribution cinematic and music content. We also fine-tune GASS models on each dataset and consistently outperform the ones without pre-training. All fine-tuned models (except the music separation one) obtain state-of-the-art results in their respective benchmarks.

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