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

共 783 条记录，以下是1-10 订阅

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. Therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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FBI-Net: Frequency Band Integration Network for Infrared small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared small Target Segmentation Semantic Injection Wavelet Transformation

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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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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： multi-branch small object detection region clipping region discriminator reinforcement learning agent

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Sixteenth International conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

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16th International conference on signal processing Systems, ICSPS 2024

ISBN: (纸本)9781510689251

The proceedings contain 197 papers. The topics discussed include: variational modal decomposition based on whale optimization algorithm for vital signs detection of FMCW radar;sea surface small target detection using tri-median normalized doppler spectrum feature;a physical cluster-based passive scatterer detection method in ISAC systems;on detection of moving weak targets using MIMO continuous active sonar and Doppler filtering;design of optical testing turntable data acquisition system based on LabVIEW;AOA /TDOA location technology of weak satellite signal based on satellite-ground cooperation;bearing-crossing localization technology for UAV target based on signal characteristics;and separation and localization of multiple bat signals using frequency domain beamforming and dual-array-localization method.

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

This letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). The median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. The three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. Through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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FBI-Net: Frequency Band Integration Network for Infrared small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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

作者： Biqiao Xin Qiang Li Qianchen Mao Jinbao 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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Wavelet transforms Frequency-domain analysis Semantics signal processing Feature extraction Excavation Robustness Real-time systems Complexity theory Speech processing

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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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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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Experimental Investigation for GPR Detection of Buried Objects with Varied Sizes and Orientation

Experimental Investigation for GPR Detection of Buried Objec...

引用

Power, Control and Computing Technologies (ICPC2T), conference on

作者： Sujeet Roy Himanshu Govil Sandip Mukherjee Prabhat Diwan Department of Applied Geology National Institute of Technology Raipur India Independent Researcher Indian Institute of Remote Sensing Dehradun India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Ground Penetration Radar (GPR) is a remote sensing technology with immense potential. Its performance and quality of results strongly depends on primarily the composition of the soil and further on other aspects of the subsurface targets like the size, material, shape, depth, and orientation. The central operating frequency of the GPR and the type of antenna on the GPR are selected based on the factors above, to be able to detect your intended target satisfactorily. Limited exploratory research work has been undertaken in this field in India towards detection of small shallow buried objects. The present study attempts to experimentally investigate the combined effect of predetermined size and orientation of small shallow buried objects in conjunction with a suitably selected central frequency of the GPR. The experiment was conducted in Jaisalmer, where the arid dry sandy wastelands are conducive to GPR performance. signal processing methods have been applied to the standard GPR results to derive better output. Apart from the standard effect of material of the targets and the depth buried to, the study mainly provided data indicating a positive correlation between the central operating frequency of the GPR and the quality of detection of the shallow subsurface targets of size corresponding to the wavelength $\lambda$. The positive correlation of polarization of the GPR antenna to the alignment of the buried object was also noted. Further investigations covering more factors affecting GPR performance is advocated by the present study.

关键词： Surveys Correlation Soil Planning data mining Test equipment Buried object detection Standards Remote sensing Antennas

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

引用

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

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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