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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11-20 订阅

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Learning DCT Subband using Kolmogorov-Arnold Network for Infrared small Target Detection 2

Learning DCT Subband using Kolmogorov-Arnold Network for Inf...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Zhang, Zekai Zhao, Yingrui Zhou, Shichao Yang, Zihui Beijing Information Science and Technology University School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Deep learning-based infrared small target detection (IRSTD) methods typically exploit spatial domain cues to infer dim and weak infrared targets. However, relying solely on spatial domain information is sub-optimal due to the lack of structure and texture details of the target. Alternatively, we learn frequency priors to enhance targets in frequency domain, and propose a Frequency Domain Enhanced Network (FDENet). It contains a frequency domain discrete cosine transform enhancement (FDE) module with KAN-inspired DCT learning mechanism. Specifically, the FDE module uses multiple univariate nonlinear functions to combine continuous multivariate functions to directly map frequency-aware cues without spatial features. It then aligns the frequency-aware cues and with the spatial features to capture dimly weak targets. Generalization experiments and comparative studies on two real-world infrared single-frame image datasets fully demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： Frequency domain analysis

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DCUNet: Deformable Convolutional UNet for Multi-frame Infrared small Target Super-resolution 2

DCUNet: Deformable Convolutional UNet for Multi-frame Infrar...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Huang, Yuanxin Zhi, Xiyang Chen, Wenbin Liang, Xiaoyang Wang, Zhipeng Zhang, Wei Harbin Institute of Technology Research Center for Space Optical Engineering Harbin China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The objective of infrared multi-frame super-resolution for small targets is to enhance the target's resolution by leveraging complementary information from multiple frames. However, the presence of motion variations and scale changes in infrared small targets introduces discontinuities in information between frames, posing challenges for super-resolution. To address the above challenges, we propose a multi-frame infrared small target super-resolution network based on deformable UNet. Specifically, the network takes multi-frame sequential infrared images as input and divides the input into reference frames and current frames. It utilizes deformable convolutions to align the frames to enhance the target features of the current frame. Additionally, the encoding structure of the UNet network is employed to extract multiscale features from the sequential images. By leveraging skip connections and a decoding structure, the network achieves the enhancement of target detail information and the fusion of multiscale features, ultimately outputting the super-resolved image. We conducted experiments on the IRDST and NUDT-MIRSDT datasets, and the results validate the practicality of our designed network. © 2024 IEEE.

关键词： Optical resolving power

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FANM: Fuzzy-Aware Nested Mamba for Infrared Dense small Target Detection 2

FANM: Fuzzy-Aware Nested Mamba for Infrared Dense Small Targ...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Sui, Yi Zhi, Xiyang Huang, Yuanxin Liang, Xiaoyang Lu, Zheng Zhang, Wei Harbin Institute of Technology Research Center for Space Optical Engineering Harbin China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The task of infrared dense small target detection aims to accurately locate densely distributed thermal radiation targets in complex scenes. However, in complex scenes, dense small targets often suffer from occlusion, shadows, and blurriness, leading to unclear detection bounding boxes and inaccurate localization. To address the aforementioned issues, this paper introduces an innovative approach for infrared dense small target detection, termed the Fuzzy-Aware Nested Mamba (FANM) network. Specifically, the network takes a single-frame infrared image as its input and leverages shallow, middle, and deep Mamba networks to fully exploit and integrate local and global multi-scale features. The extracted features are subsequently passed to a feature pyramid network (FPN) to balance the spatial resolution and semantic information of each feature map layer, ensuring that feature maps of different scales can be effectively utilized. Then, the output features at each level are fed into the detection head, which consists of two branches: one for regressing the positions of arbitrarily distributed bounding boxes, and the other for predicting the quality of the bounding boxes. The proposed fuzzy perceptual loss function is used to optimize these branches, ultimately outputting images with accurately detected target bounding boxes. Experiments were performed on the DMIST dataset, which includes images with varying numbers of targets, and the results validate the effectiveness of our method. © 2024 IEEE.

关键词： Infrared imaging

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Research on Complex Street View Detection Algorithm Based on YOLOv8 2

Research on Complex Street View Detection Algorithm Based on...

引用

2nd International conference on signal processing and Intelligent Computing, SPIC 2024

作者： Du, Haiyang Liu, Jun Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China

ISBN: (纸本)9798350368888

The current street scene is becoming more and more complex, with high pedestrian and vehicular traffic and a large number of targets. Aiming at the problems that the accuracy of the current complex street scene-oriented detection algorithms is not satisfactory and the leakage rate of small targets is unstable, the intelligent detection algorithm YOLOv8s- for complex street scenes is proposed, aiming at the intelligent detection of multi-targets in daily street scenes. This study is based on the target detection model of YOLOv8s, proposing a C2f-DCNv2 module that can sample features flexibly;adding a small target detection layer in the head;and using the Shape IoU loss function. The improved algorithm improves the precision, recall, and average accuracy by 2.2%, 3.6%, and 5.0%, respectively, compared with the initial algorithm, and these experimental data validate that this study can have a good performance when targeting the detection task of complex street scenes. © 2024 IEEE.

关键词： deformable convolution shape-IoU small target detection street view detection YOLOv8

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A Low-Altitude small Target Radar Recognition Algorithm Based on Micro-Doppler Features and Vision Transformer Network 2

A Low-Altitude Small Target Radar Recognition Algorithm Base...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Chen, Guangmao Liu, Haibo Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing100081 China Beijing Institute of Technology Advanced Technology Research Institute Jinan250300 China

ISBN: (纸本)9798331515669

In low-altitude airspace surveillance scenarios, accurately distinguishing between birds and unmanned aerial vehicles (UAVs) is of vital importance. However, the high similarity in flight altitude and speed between birds and UAVs makes it extremely difficult to distinguish between these two types of targets. To enhance radar recognition performance for both UAVs and birds, this paper proposes a low-altitude target recognition algorithm that combines micro-Doppler features with a Vision Transformer network (ViT) under a staring radar system. Verified with real-measured data, the recognition accuracy for three types of UAVs and birds reaches 94%. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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

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

引用

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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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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Dual-Level Contrast Enhancement Method for Infrared small Target Detection 2

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

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Alarm systems

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Multi-view Bistatic SAR Automatic Recognition Study based on Ground small Target SAR dataset 2

Multi-view Bistatic SAR Automatic Recognition Study based on...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Lv, Jiming Zhu, Daiyin Nanjing University of Aeronautics and Astronautics College of Electronics and Information Engineering Nanjing China Niigata University Faculty of Engineering Niigata Japan Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Nanjing China

ISBN: (纸本)9798331515669

MiniSAR systems possess unique advantages in ground target detection compared to traditional spaceborne and airborne SAR systems, mainly in terms of lower cost, real-time performance, and portability. Although numerous studies have focused on target recognition based on SAR systems with unmanned aerial vehicle, most are limited to monostatic SAR systems. Bistatic SAR systems offer stronger stealth capabilities compared to monostatic systems, as they can receive electromagnetic reflections from any angle. However, due to the lower intensity of received electromagnetic signals in bistatic systems, targets in SAR images tend to experience deformation and attenuation. In this study, based on independently developed monostatic and bistatic SAR systems, we study the integration of multi-view and large-scale deep learning models to aiming to address the issue of hard to recognized small targets. Additionally, the paper validates our proposed method on the publicly available MSTAR dataset and the self-developed FAST-Vehicle dataset. Experimental results demonstrate that the proposed multi-view target recognition method effectively identifies SAR bistatic deformed targets. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multiband Radar signal Fusion and Extrapolation Method Based on Transformer Model 2

Multiband Radar Signal Fusion and Extrapolation Method Based...

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2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Drones

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