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IR-MPE: A Long-Term Optical Flow-Based Motion Pattern Extractor for Infrared small Dim targets

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Xuyang Zhu, Wenming Yan, Pei Tan, Yihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Multispectral Informat Intelligent Pr Wuhan 430074 Peoples R China

In complex scenarios, the utilization of temporal motion information can improve the detection performance of infrared small and dim targets. However, existing multiframe methods only consider short-term motion information at each moment, which is difficult to capture reliable motion information for small and dim targets. In addition, existing multiframe data-driven methods generally utilize complex network structures which have longer inference times compared with the single-frame models of infrared target detection. Such a problem limits the applicability of the existing multiframe methods. In this article, we propose a long-term optical flow (OF)-based infrared small target motion pattern extractor (IR-MPE) to generate long-term OF energy (OFE) maps, which reflect the motion patterns of targets at the current moment. First, we design a long-term OF adaptive accumulation module (OFAAM) to adaptively control the update of current motion information and the retention of previous motion information. Second, we design an offset correction module (OCM), which is embedded in the OFAAM module to rectify the OFE from the previous frame. Meanwhile, the OCM also corrects the output of the previous frame to assist in the detection of the current frame. Embedding our IR-MPE module into the existing single-frame methods can easily extend them as multiframe methods. The only modification is adding an extra input channel of the first layer whose input is set as the concatenation of our OFE and the original infrared image. Such a simple structure can significantly improve the detection accuracy while maintaining fast inference speed. Extensive experiments on various public datasets show that our approach outperforms the state-of-the-art methods in challenging scenarios.

关键词： data mining Optical flow Object detection Feature extraction Deep learning Fuses Detectors Dams Accuracy signal to noise ratio Infrared small dim target detection long-term optical flow (OF) motion pattern extractor

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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...

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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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Cross-YOLO: an object detection algorithm for UAV based on improved YOLOv8 model

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signal IMAGE AND VIDEO processing 2025年第6期19卷 1-10页

作者： Dong, Ying Guo, Jiahao Xu, Fucheng Dalian Minzu Univ Coll Sci 31 Jinshi Rd Dalian 116000 Liaoning Peoples R China Dalian Minzu Univ Sch Preuniv 31 Jinshi Rd Dalian 116000 Liaoning Peoples R China

In this study, Cross-YOLO, an enhanced version of the YOLOv8 model, is specifically designed to address the challenge of detecting small objects in UAV target detection scenarios. The model refines the original YOLOv8 through several innovative improvements: Firstly, in order to improve the detection accuracy of small targets, we propose Cross-FPN to bolster the original FPN. Secondly, we have redesigned a lightweight detection head, DELDH, to solve the problem of network bloat caused by the introduction of small object detection heads. Thirdly, a new attention mechanism CMCA is designed, that unifies the Coordinate attention mechanism with the Multi-scale convolutional attention mechanism to further enhance the feature extraction of small targets. Finally, the WIoU loss function is introduced to improve the accuracy of bounding box regression and improve detection performance. Experimental data in the Visdrone dataset indicate that, under the condition of the selected model size n, Cross-YOLO achieves a substantial reduction of 35.4% in parameter count compared to YOLOv8n, with only a marginal increase of 7.8% in computational load, and a significant improvement of 5.3% in mAP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$mAP_{0.5}$$\end{document}. Furthermore, its strong performance on the DOTA v1.5 and TinyPerson datasets confirms the model's generalization capabilities and practical applicability.

关键词： YOLOv8 small object Object detection UAV Attention

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An NN Framework for Target Number Detection in FMCW Radar Systems via Hankelization: A Low-Dimensional data Representation Perspective

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IEEE ACCESS 2025年 13卷 58823-58834页

作者： Kim, Jung-Hwan Kim, Gyung-Eun Lee, Woong-Hee Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea

In this paper, we introduce a novel neural network (NN)-based algorithm that significantly improves the target number detection in frequency modulated continuous wave (FMCW) radar systems. By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. This is based on the following mathematical properties: 1) A sequence obtained by uniform sampling of the superposition of K radio waves can be represented as a superposition of K geometric sequences;2) A Hankelized matrix formed by the superposition of K geometric sequences exhibits low-rank characteristics;and 3) In an FMCW radar system with K targets, if the received signal, which is represented as a matrix, is ideal, the vectors obtained by extracting this matrix in row, column, diagonal, and anti-diagonal patterns can all be modeled as a superposition of K geometric sequences. The proposed NN framework showcases remarkable improvements in accuracy and efficiency for target number detection, leveraging a small sized dataset and a compact NN design to achieve unprecedented performance levels. Numerical results validate the superiority of our method across various scenarios, establishing a new benchmark for low-dimensional data representation in radar systems.

关键词： Radar Estimation Accuracy Heuristic algorithms Radar detection Radar antennas Artificial neural networks Vectors signal processing algorithms data visualization Hankelization singular value extraction neural networks radar signal processing

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small object detection in UAV imagery based on channel-spatial fusion cross attention

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signal IMAGE AND VIDEO processing 2025年第4期19卷 1-14页

作者： Li, Jianlong Zheng, Chunhou Chen, Peng Zhang, Jun Wang, Bing Anhui Univ Inst Phys Sci & Informat Technol Natl Engn Res Ctr Agroecol Big Data Anal & Applica Sch InternetInformat Mat & Intelligent Sensing La Hefei 230601 Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Peoples R China Anhui Rocvis Intelligent Technol Co Ltd Hefei 230001 Peoples R China

Object detection in unmanned aerial vehicle (UAV) images has become an important research area in computer vision due to its unique value and challenges. UAV images are characterized by densely distributed small targets, significant changes in target scale, and background noise, which affect the accuracy and reliability of detection. To address these issues, we propose an small target detection network based on Enhanced Scale Sequence Fusion and channel space fusion cross-attention mechanism, called *** tackle the high proportion of small targets and scale variation in UAV images, we employ Enhanced Scale Sequence Fusion, integrating fine-grained information from shallow feature maps and semantic information from deep feature maps. Additionally, we incorporate an tiny target detection head to enhance the network's ability to extract fine-grained information features for small targets. To address the issue of background noise, we propose a channel space fusion cross-attention mechanism, which first performs attention calculation on local patch block feature maps, and then performs attention calculation global patch blocks. This captures both long-range dependencies and detailed information. The method for calculating attention combines spatial description information and channel description *** experiments were conducted to validate the effectiveness of the model on the VisDrone benchmark dataset, UAVDT dataset and our self-made UAV power inspection dataset PIDrone. In comparison to the YOLOv8s model, the CSFCANet demonstrated an improvement in mAP of 7% on the PIDrone, 2.4% on the VisDrone, and 3.6% on the UAVDT.

关键词： small object detection Attention mechanism Multi-scale feature fusion UAV Aerial Imagery

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Enhanced YOLOv8 for small-object detection in multiscale UAV imagery: Innovations in detection accuracy and efficiency

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DIGITAL signal processing 2025年 158卷

作者： Luo, Weixin Yuan, Sannan Shanghai Univ Elect Power Sch Elect & Informat Engn Shanghai 201306 Peoples R China

Due to the ability of drone technology to cover wide areas and reach difficult-to-access places, the detection of small targets by Unmanned Aerial Vehicles (UAVs) is crucial in various applications such as urban management planning and emergency response. This paper proposes innovations to the YOLOv8 architecture, significantly enhancing its performance in small target detection from multi-scale drone perspectives. We introduce Channel Priority Attention Dynamic Snake Convolution and Dynamic small Object Detection Head Layer (DyHead-SODL) to improve the model's ability to capture fine details and detection accuracy. Additionally, we implemented an enhanced loss function (MPDIoU) and a Deformable Attention Transformer (DAT) to optimize detection efficiency without increasing computational burden. Experimental results on the Visdrone and RSOD datasets demonstrate significant improvements, with the proposed method increasing mAP50 by 10.5 %, mAP95 by 6.9 %, reducing localization error Eloc by 1.27, and decreasing the miss rate by 2.42 on the Visdrone dataset, outperforming existing state-of-the-art detection models while maintaining low computational complexity. The proposed method has also been tested on four large public datasets: DOTA, HRSC, LEVIR, and CARPK, demonstrating the generalization capability of our model. These advances provide effective solutions for small target detection from drone perspectives and promote the development of single-stage object detection technology. These advancements provide an effective solution for small object detection from UAVs, advancing the field of one-stage object detection technology. (source code: https://***/GuccIceCream/yolov8/tree/master)

关键词： YOLOv8 small object detection Channel prioritized attention dynamic snake convolution Dynamic small object detection head MPDIoU Deformable attention transformer

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Copy-Move Detection in Optical Microscopy: A Segmentation Network and a dataset

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IEEE signal processing LETTERS 2025年 32卷 1106-1110页

作者： Shao, Hao-Chiang Liao, Yuan-Rong Tseng, Tse-Yu Chuo, Yen-Liang Lin, Fong-Yi Natl Chung Hsing Univ Inst Data Sci & Informat Comp Taichung 402202 Taiwan SOE Technol Inc Taipei 106 Taiwan IGrp Asia Pacific Ltd Taipei 104 Taiwan

With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen attacks, rendering standard object-detection-based approaches less effective. To address this, we reformulate the problem of detecting biomedical copy-move forgery regions as an intra-image co-saliency detection task and propose CMSeg-Net, a copy-move forgery segmentation network capable of identifying unseen duplicated areas. Built on a multi-resolution encoder-decoder architecture, CMSeg-Net incorporates self-correlation and correlation-assisted spatial-attention modules to detect intra-image regional similarities within feature tensors at each observation scale. This design helps distinguish even small copy-move targets in complex microscopic images from other similar objects. Furthermore, we created a copy-move forgery dataset of optical microscopic images, named FakeParaEgg, using open data from the ICIP 2022 Challenge to support CMSeg-Net's development and verify its performance. Extensive experiments demonstrate that our approach outperforms previous state-of-the-art methods on the FakeParaEgg dataset and other open copy-move detection datasets, including CASIA-CMFD, CoMoFoD, and CMF.

关键词： Forgery Feature extraction Tensors Microscopy Image segmentation Optical microscopy Correlation Decoding Fraud Optical imaging Copy-move forgery detection attention multiresolution optical microscopy images co-saliency

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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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Robust Range Super-Resolution Imaging With Multicriteria Joint Constraints via Double Smoothed l0-Norm Under Limited Resources

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 5145-5165页

作者： Liu, Zhexian Shao, Shuai Liu, Hongwei Su, Tao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The radar's high-resolution range profile (HRRP) contains rich physical structural features of targets, making it of great value for target recognition and classification. However, in practical applications, the effective bandwidth of radar signals is limited due to the nonideal factors such as spectrum control, limited hardware equipment capabilities, and spectrum electromagnetic interference. Thus, it is difficult to reach the requirement of high-resolution and accurate target characterization in range profiles under limited resources, which will reduce subsequent detection, imaging, and recognition performance. To address the problem, this article proposes a robust range profile super-resolution method with multicriteria joint constraints of sparsity, low-rank, and Doppler under a multiple measurement vector model. In this technique, based on compressed sensing theory and adaptive filtering framework, a sparse reconstruction algorithm via double smoothed l(0)-norm is proposed, which constructs a continuously differentiable Gaussian class function to approximate the l(0)-norm. It combines accelerated gradient algorithm and zero attractor to optimize the multicriteria cost function and iteratively reconstruct the super-resolution range profile. The algorithm improves the super-resolution performance of range profiles, enhances the robustness against noise and complex range profile configurations, and can obtain high-precision HRRP of targets with limited radar resources. Extensive simulation and real data experiments demonstrate that it not only improves the average reconstruction accuracy of super-resolution range profile sequence, but effectively reduces the fluctuation of reconstruction errors. In addition, it does not require high azimuth resolution, and is also highly effective with a small number of sequence echoes.

关键词： Image reconstruction Superresolution Radar Accuracy Target recognition signal processing algorithms Scattering Radar imaging Vectors Doppler effect Compressed sensing (CS) double smoothed l(0) -norm (DSL0) high-resolution range profile (HRRP) limited resource multicriteria joint constraints super-resolution imaging

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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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