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Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction

清华大学学报自然科学版（英文版） 2025年第3期30卷 1174-1189页

作者： Wei Xue Xiaoyan Xia Pengcheng Wan Ping Zhong Xiao Zheng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense TechnologyChangsha 410073China

Deep neural networks are commonly used in computer vision tasks,but they are vulnerable to adversarial samples,resulting in poor recognition *** traditional algorithms that craft adversarial samples have been effective in attacking classification models,the attacking performance degrades when facing object detection models with more complex *** address this issue better,in this paper we first analyze the mechanism of multi-scale feature extraction of object detection models,and then by constructing the object feature-wise attention module and the perturbation extraction module,a novel adversarial sample generation algorithm for attacking detection models is ***,in the first module,based on the multi-scale feature map,we reduce the range of perturbation and improve the stealthiness of adversarial samples by computing the noise distribution in the object *** in the second module,we feed the noise distribution into the generative adversarial networks to generate adversarial perturbation with strong attack *** doing so,the proposed approach possesses the ability to better confuse the judgment of detection *** carried out on the DroneVehicle dataset show that our method is computationally efficient and works well in attacking detection models measured by qualitative analysis and quantitative analysis.

关键词： adversarial attack transfer attack object detection generative adversarial networks multi-scale feature map

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FPGA implementation of a Bernoulli particle filter

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2025年第2期47卷 398-405页

作者： Lian, Hongfei Li, Dongsheng Jiang, Yanwen Fan, Hongqi Xiao, Huaitie Wang, Guoyan National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Aiming at the high-speed and efficient computing problem of Bernoulli particle filters in embedded application environments, taking the Bernoulli particle filters for joint detection and estimation of radar weak targets as an example, a function modularized and particle size scalable field programmable gate array (FPGA) implementation architecture is proposed. The computing speed of filtering calculation is further improved through approaches as particle state pipelining, layered accumulation and sum, and parallel resampling, etc. Xilinx ZC706 evaluation board on-board testing experiments have demonstrated the good scalability and excellent acceleration ratio of the proposed architecture. When the number of particles is 1 024, the acceleration ratio is about 104 orders of magnitude compared to the Intel Core i3-4130 CPU computing environment. The results have important reference value for the application of Bernoulli particle filtering technology in radar, robotics, and navigation guidance fields. © 2025 Chinese Institute of Electronics. All rights reserved.

关键词： Electronic guidance systems

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T2TNet: Tree-to-Transformer Neural Network for High-precision Prediction of Ship Machinery Noise 24

T2TNet: Tree-to-Transformer Neural Network for High-precisio...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Liu, Chang Zhao, Guo Wang, Lei Xie, Xiaotian Chen, Hailong Xie, Yuxin Gao, Fan Tang, Yang Lang, Jicai Yantai Research Institute Harbin Engineering University Yantai China National Key Laboratory of Automatic Target Recognition Beijing Huahang Radio Measurement Institute Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798400710681

Traditional ship machinery noise prediction methods face many challenges, including cumbersome data processing procedures, numerous complex coupling factors, and poor generalization capabilities. In response to these issues, this paper proposes an innovative ship machinery noise prediction algorithm, named as Tree-to-Transformer Network (T2TNet). We first utilize an ensemble learning algorithm to learn the low-dimensional feature association knowledge, and then employs the Transformer network to deeply explore the high-dimensional global knowledge relationships, thereby enhancing the predictive capability for ship machinery noise prediction. Experimental results show that the algorithm achieves a prediction accuracy rate as high as 99.9% within a 3dB error range, with an overall average accuracy rate of 99.6%, demonstrating excellent predictive performance and noise forecasting ability. This achievement not only meets the basic requirements of the ship noise prediction field but also provides strong technical support for ship design and maintenance. © 2024 Copyright held by the owner/author(s).

关键词： Predictive maintenance

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Active Object Detection for UAV Remote Sensing via Behavior Cloning and Enhanced Q-Network with Shallow Features

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Zou, Zhuocheng Hu, Xikun Zhong, Ping National University of Defense Technology National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology Changsha410073 China

Object detection in Unmanned Aerial Vehicle (UAV) remote sensing imagery faces two critical challenges: the inability to effectively handle multi-scale targets and the difficulty in addressing partial occlusions, which significantly impact detection accuracy in real-world applications. To overcome these limitations, we propose an active object detection (AOD) method that dynamically adjusts viewing angles and scales during the detection process. Our key innovation lies in a novel architecture that uniquely combines shallow Feature Pyramid Network features with detector output bounding boxes, combining with a self-supervised learning framework. The technical originality of our approach is further enhanced by our two-stage learning methodology, which initially employs behavior cloning to establish robust foundational performance, followed by Q-learning fine-tuning to optimize detection strategies. To facilitate comprehensive evaluation, we introduce CARLA-AOD, a new benchmark dataset that encompasses 18 diverse scenarios across hemispheric space above targets, specifically designed for UAV remote sensing AOD applications. Extensive experimental validation demonstrates the effectiveness of our approach, achieving substantial improvements over baseline detectors across multiple datasets: 7.9% on Small Airport, 14.0% on Virtual Park, and 10.2% on our CARLA-AOD dataset. The two-stage learning process proves particularly effective, with Q-learning fine-tuning providing an additional performance boost of up to 7.3% beyond the behavior cloning baseline. Moreover, our method achieves a 60.2% reduction in inference time compared to the state-of-the-art DCCL algorithm, making it particularly suitable for time-critical applications such as emergency response and real-time surveillance. The dataset is available at IEEE Dataport. IEEE Dataport. © 2025 IEEE.

关键词： Self-supervised learning

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Nearshore Underwater target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset

arXiv

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arXiv 2025年

作者： Qi, Jiahao Zhou, Chuanhong Liu, Xingyue Chen, Chen Zhu, Dehui Bin, Kangcheng Zhong, Ping National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha410073 China

UAV-borne hyperspectral remote sensing has emerged as a promising approach for underwater target detection (UTD). However, its effectiveness is hindered by spectral distortions in nearshore environments, which compromise the accuracy of traditional hyperspectral UTD (HUTD) methods that rely on bathymetric model. These distortions lead to significant uncertainty in target and background spectra, challenging the detection process. To address this, we propose the Hyperspectral Underwater Contrastive Learning Network (HUCLNet), a novel framework that integrates contrastive learning with a self-paced learning paradigm for robust HUTD in nearshore regions. HUCLNet extracts discriminative features from distorted hyperspectral data through contrastive learning, while the self-paced learning strategy selectively prioritizes the most informative samples. Additionally, a reliability-guided clustering strategy enhances the robustness of learned representations. To evaluate the method effectiveness, we conduct a novel nearshore HUTD benchmark dataset, ATR2-HUTD, covering three diverse scenarios with varying water types and turbidity, and target types. Extensive experiments demonstrate that HUCLNet significantly outperforms state-of-the-art methods. The dataset and code will be publicly available at: https://***/qjh1996/HUTD. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

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Journal of Systems Engineering and Electronics 2019年第4期30卷 684-695页

作者： ZHANG Hongwei XIE Weixin Automatic Target Recognition Key Laboratory Shenzhen University

To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is *** restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 ***,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the *** take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted *** a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier *** results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

关键词： bearings-only maneuvering target tracking soft measurement constraints constrained auxiliary particle filtering(CAPF)

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Design and implementation of the optical fiber control and transmission module in multi-channel broadband digital receiver

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Optoelectronics Letters 2014年第5期10卷 369-373页

作者：赵英潇张月范立杰李卫星陈曾平 State Key Laboratory of Automatic Target Recognition National University of Defense Technology

An optical fiber control and transmission module is designed and realized based on Virtex-7 field programmable gata array(FPGA), which can be applied in multi-channel broadband digital receivers. The module consists of sampling data transfer submodule and multi-channel synchronous sampling control submodule. The sampling data transmission in 4× fiber link channel is realized with the self-defined transfer protocol. The measured maximum data rate is 4.97 Gbyte/s. By connecting coherent clocks to the transmitter and receiver endpoints and using the self-defined transfer protocol, multi-channel sampling control signals transferred in optical fibers can be received synchronously by each analog-to-digital converter(ADC) with high accuracy and strong anti-interference ability. The module designed in this paper has certain reference value in increasing the transmission bandwidth and the synchronous sampling accuracy of multi-channel broadband digital receivers.

关键词： Analog to digital conversion Data transfer Field programmable gate arrays (FPGA) Light transmission

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Range anomaly suppression based on neighborhood pixels detection in ladar range images

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Journal of Systems Engineering and Electronics 2013年第1期24卷 68-75页

作者： Mingbo Zhao Jun He Zaiqi Lu Qiang Fu Automatic Target Recognition Key Laboratory School of Electronic Science and EngineeringNational University of Defense Technology No.93246 Troop of the PLA

Research on the range anomaly suppression algorithm in laser radar （ladar） range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algo- rithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighbor- hood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms.

关键词： image processing range anomaly suppression neigh-borhood p xe s detection linear target laser radar (ladar).

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Reconstruction of time series with missing value using 2D representation-based denoising autoencoder

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Journal of Systems Engineering and Electronics 2020年第6期31卷 1087-1096页

作者： TAO Huamin DENG Qiuqun XIAO Shanzhu National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic ScienceNational University of Defense TechnologyChangsha 410073China

Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction ***,missing data frequently occur during data recording,posing a great challenge to data mining *** this study,we propose a novel time series data representation-based denoising autoencoder(DAE)for the reconstruction of missing *** data representation methods,namely,recurrence plot(RP)and Gramian angular field(GAF),are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time *** an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series.A comprehensive comparison is conducted amongst the different representations on standard *** show that the 2D representations have a lower reconstruction error than the raw time series,and the RP representation provides the best *** work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.

关键词： time series missing value 2D representation denoising autoencoder(DAE) reconstruction

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Reflection separation technology based on polarization characteristics

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Journal of Systems Engineering and Electronics 2022年第5期33卷 1032-1042页

作者： ZHANG Yan ZHANG Jinghua SHI Zhiguang ZHANG Yu LING Feng National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and EngineeringNational University of Defense TechnologyChangsha 410073China

Specific to the reflected light problem on the surface of transparent body,the polarization characteristics of the reflection region are analyzed,and a polarization characterization model combining the reflection and transmission effects is *** the basis of the polarization characteristic analysis,the minimum value of normalized cross-correlation(NCC)coefficient between transmission and reflection images is solved through the gradient descent method,and their polarization degrees under the minimum correlation are *** to the distribution relations of the transmitted and reflected lights in perpendicular and parallel directions,reflection separation is realized via the polarized orthogonality difference algorithm based on the degree of reflection polarization and the degree of transmission polarization.

关键词： reflection separation transparent object correlation polarization characteristics

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