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检索条件"任意字段=13th Conference on Signal and Data Processing of Small Targets"

共 447 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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LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED small TARGET DETECTION 49

LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED SMA...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Qiang Mao, Qianchen Liu, Wenjie Wang, Jinbao Wang, Wenmin Wang, Bingshu Northwestern Polytech Univ Sch Software Xian Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

ISBN: (纸本)9798350344868;9798350344851

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.

关键词： infrared small target detection local inductive bias information context and cross-level fusion Swin Transformer

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Research on Image Quality Assessment Methods for small Target data Augmentation under the YOLO Framework 24

Research on Image Quality Assessment Methods for Small Targe...

引用

13th International conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Zhibin Liu, Pengyu Cui, Bifeng Li, Gang Deng, Jun Li, Qian Department of Information Science Beijing University of Technology Beijing China

ISBN: (纸本)9798400717482

When using image scaling algorithms for data augmentation on small targets within the YOLOv framework, it was observed that the ranking of different image scaling algorithms based on their data enhancement effects significantly diverges from the rankings provided by mainstream image quality assessment methods based on the human visual system. this indicates that the machine vision system of YOLOv5 has different requirements for image quality compared to the human visual system. By analyzing various image quality assessment methods, this study identified an image quality assessment method, BLIINDS-II, that is highly correlated with the data enhancement effects of YOLOv5. It was found that YOLOv5's assessment of image quality is derived from the non-DC coefficients of the image following a Discrete Cosine Transform (DCT), focusing on the high-frequency components of the image. © 2024 Copyright held by the owner/author(s).

关键词： Discrete cosine transforms

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An Effective Crop Diseases Detector via CropDetDiff Model with Multi-Sized Selective Kernels 13

An Effective Crop Diseases Detector via CropDetDiff Model wi...

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13th IEEE data Driven Control and Learning Systems conference, DDCLS 2024

作者： Hua, Chenghao Kong, Jianlei National Engineering Research Center for Agri-Product Quality Traceability Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9798350361674

Crop disease detection is an integral aspect of smart agricultural practices, instrumental in augmenting crop yield and quality. Contemporary mainstream approaches are heavily reliant on deep learning paradigms whose enhancements are contingent upon substantial volumes of meticulously annotated datasets. However, in the domain of agricultural pathology detection, the arduous task of dataset creation and the inherent challenges associated with the detection of small targets pose formidable obstacles to existing methodologies. In response to these challenges, we introduce a detector named CropDetDiffthat employs diffusion learning to minimize reliance on training samples, thus delivering superior detection performance in agricultural settings with limited training data availability. To address the detection of small-sized crop targets, we have developed a lightweight graph neural network, LCSP-ViG, with an integrated Large-Sized Selective Kernel (LSK) network for sparse detection of small objects. We have also designed a One-to-Few (O2F) head featuring a label assignment strategy to enhance the precision of small object bounding boxes. Finally, a new bounding box loss function, HIoU, has been introduced to focus the model's supervised learning specifically on small objects. We conducted a series of experiments that verified the optimal detection performance of our model compared to other leading detectors. In particular, on the agricultural object detection datasets Strawberry, our model demonstrated APs of 73.16%, notably, there is a pronounced enhancement in detection performance of small targets. the performance of this model proves its effectiveness for the operation on agricultural robots. © 2024 IEEE.

关键词： agriculture disease detector Diffusion model large selective kernel one-to-few assignment small object detection

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GASS: GENERALIZING AUDIO SOURCE SEPARATION WIth LARGE-SCALE data 49

GASS: GENERALIZING AUDIO SOURCE SEPARATION WITH LARGE-SCALE ...

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Pons, Jordi Liu, Xiaoyu Pascual, Santiago Serra, Joan Dolby Labs San Francisco CA 94103 USA

ISBN: (纸本)9798350344868;9798350344851

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.

关键词： General audio source separation deep learning

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AntoNet: a small linear contrast-enhancing CNN for pixel-wise detection 13

AntoNet: a small linear contrast-enhancing CNN for pixel-wis...

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13th International conference on Image processing theory Tools and Applications

作者： Antonio, Soggia Lionel, Fillatre Laurent, Deruaz-Pepin Thales DMS I3S Sophia Antipolis France Univ Cote Azur I3S CNRS Sophia Antipolis France Thales DMS Sophia Antipolis France

ISBN: (纸本)9798331541859;9798331541842

In current broadband passive sonar systems, a human operator observes a 2D image that represents different energy levels in the bearing and time dimensions. the temporal depth of the image helps the operator to detect and identify the paths taken by different sources. However, detecting weak noise sources amid ambient sea noise and strong sources can be challenging. the purpose of this work is to increase the contrast of those images using AI techniques. the performance of AntoNet, our proposed architecture based on Unet, is evaluated for reducing ambient noise and enhancing the signal of interest. the dataset is generated by a fast sonar image simulator based on a simplified sonar beamforming model. We aim to improve pixel-wise detection through clearer 2D images with a better SNR. AntoNet leverages the temporal depth of data to produce 2D images with an enhanced SNR while having less than 1,500 parameters. Performance evaluation demonstrates AntoNet's effectiveness in single source environments, achieving higher detection performance with minimal artifacts.

关键词： CNN detection contrast enhancement sonar

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD) 6

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

引用

6th International conference on Intelligent Control, Measurement and signal processing, ICMSP 2024

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

ISBN: (纸本)9798331516550

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. © 2024 IEEE.

关键词： Motion compensation

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Long-time Adaptive Coherent Detection of small targets in Sea Clutter using Inversion of Block Tridiagonal Covariance Matrices 15

Long-time Adaptive Coherent Detection of Small Targets in Se...

引用

15th International conference on signal processing Systems, ICSPS 2023

作者： Zhang, Xiaojun Xue, Yufan Shui, Penglang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510675056

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. the proposed detector is verified by an X-band CSIR radar database with low-velocity small boats as test targets. the experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors. © 2024 SPIE.

关键词： Covariance matrix

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Autoencoder-based Joint Communication and Sensing of Multiple targets 26

Autoencoder-based Joint Communication and Sensing of Multipl...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th conference on Systems, Communications, and Coding, SCC 2023

作者： Muth, Charlotte Schmalen, Laurent Hertzstr. 16 Karlsruhe76187 Germany

ISBN: (纸本)9783800760510

for building a joint communication and sensing (JCAS) system that enables communication with one user while detecting multiple radar targets and estimating their positions. Foremost, we develop a suitable encoding scheme for the training of the AE and for targeting a fixed false alarm rate of the target detection during training. We compare this encoding with the classification approach using one-hot encoding for radar target detection. Furthermore, we propose a new training method that complies with possible ambiguities in the target locations. We consider different options for training the detection of multiple targets. We can show that our proposed approach based on permuting and sorting can enhance the angle estimation performance so that single snapshot estimations with a low standard deviation become possible. We outperform an ESPRIT benchmark for small numbers of measurement samples. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： signal encoding

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Recognition of signal Modulation Pattern Based on Multi-task Self-supervised Learning 13th

Recognition of Signal Modulation Pattern Based on Multi-task...

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13th IFIP TC 12 International conference on Intelligent Information processing, IIP 2024

作者： Cheng, Dianjing Wu, Xingyu Xie, Zhenghao Cui, Zhihua Li, Qiong Tong, Endong Niu, Wenjia Wei, Ziyi Zhao, Xinyi Taiyuan University of Science and Technology Taiyuan China Beijing Jiaotong University Beijing China Hebei University Baoding China

ISBN: (纸本)9783031579189

In wireless communication, the recognition of signal modulation plays an essential role. However, acquiring high-quality data in wireless communications is often prohibitively expensive and challenging. Traditional methods for modulation pattern recognition are limited by specialized knowledge, resulting in poor adaptability and generalization. Although deep neural networks demonstrate superior performance in modulation pattern recognition, they heavily depend on high-quality and accurately annotated training data. they require significant computational resources during training, rendering them unsuitable for resource-constrained devices or real-time applications. We propose a signal modulation pattern recognition method based on multi-task self-supervised learning to overcome these challenges. this approach begins by enhancing data from various unlabeled categories, then capturing the essential signal characteristics through contrastive learning to obtain a robust pre-trained model. We then fine-tune the model with a small account of labeled modulation samples to better adapt it to downstream tasks. Experimental results indicate that in scenarios with limited sample availability, our method slightly surpasses traditional recognition methods in accuracy and shows significant advantages in training efficiency. © IFIP International Federation for Information processing 2024.

关键词： Pattern recognition

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