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Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

Chinese Journal of Electronics 2024年第4期33卷 997-1009页

作者： Yanshan LI Jiarong WANG Kunhua ZHANG Jiawei YI Miaomiao WEI Lirong ZHENG Weixin XIE ATR National Key Laboratory of Defense Technology Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

Existing high-precision object detection algorithms for UAV(unmanned aerial vehicle) aerial images often have a large number of parameters and heavy weight, which makes it difficult to be applied to mobile *** propose three YOLO-based lightweight object detection networks for UAVs, named YOLO-L, YOLO-S, and YOLO-M, respectively. In YOLO-L, we adopt a deconvolution approach to explore suitable upsampling rules during training to improve the detection accuracy. The convolution-batch normalization-Si LU activation function(CBS)structure is replaced with Ghost CBS to reduce the number of parameters and weight, meanwhile Maxpool maximum pooling operation is proposed to replace the CBS structure to avoid generating parameters and weight. YOLO-S greatly reduces the weight of the network by directly introducing CSPGhost Neck residual structures, so that the parameters and weight are respectively decreased by about 15% at the expense of 2.4% mAP. And YOLO-M adopts the CSPGhost Neck residual structure and deconvolution to reduce parameters by 5.6% and weight by 5.7%, while m AP only by 1.8%. The results show that the three lightweight detection networks proposed in this paper have good performance in UAV aerial image object detection task.

关键词： Training YOLO Deconvolution Accuracy Object detection Autonomous aerial vehicles Feature extraction

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Decentralized fault diagnosis of discrete-event systems with unreliable sensors using linear temporal logic

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Science China(information Sciences) 2025年第1期68卷 322-341页

作者： Weijie DONG Shaoyuan LI Xiang YIN Key Laboratory of System Control and Information Processing Ministry of EducationDepartment of Automation Shanghai Jiao Tong University

In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of possible *** studies on decentralized robust diagnosis can only deal with different types of sensor failures separately,e.g.,all sensors suffer from the same type of sensor failures such as intermittent sensor failures or permanent sensor ***,since sensors of different local diagnosers may face different external environments and have different internal characteristics,sensors corresponding to different local diagnosers may also have their own fault *** this paper,we propose a flexible framework of decentralized diagnosis for DES subject to unreliable sensors such that sensors of different local diagnosers are permitted to have different types of sensor *** this end,we extend the existing decentralized diagnosis framework to the case where there exist common sensors broadcasting their observations to all local *** apply linear temporal logic(LTL) to constrain infinite behaviors of private sensors of local diagnosers and common ***,a new notion of φ-codiagnosability is proposed as the necessary and sufficient condition for the existence of a decentralized diagnoser that works correctly under sensors,satisfying LTL-based sensor ***,we provide an effective approach to verify the φ-codiagnosability.

关键词： discrete event systems decentralized diagnosis codiagnosability automata

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Value Iteration-Based Distributed Adaptive Dynamic Programming for Multi-Player Differential Game With Incomplete information

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 436-447页

作者： Yun Zhang Yuqi Wang Yunze Cai the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China the National Key Laboratory of Air-based Information Perception and Fusion

In this paper, a distributed adaptive dynamic programming(ADP) framework based on value iteration is proposed for multi-player differential games. In the game setting,players have no access to the information of others' system parameters or control laws. Each player adopts an on-policy value iteration algorithm as the basic learning framework. To deal with the incomplete information structure, players collect a period of system trajectory data to compensate for the lack of information. The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy. Theoretical analysis shows that by adopting proximal policy searching rules, the approximated policies can converge to a neighborhood of equilibrium policies. The efficacy of our method is illustrated by three examples, which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.

关键词： Distributed adaptive dynamic programming incomplete information multi-player differential game (MPDG) value iteration

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Echo state network based symbol detection in chaotic baseband wireless communication

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Digital Communications and Networks 2023年第6期9卷 1319-1330页

作者： Huiping Yin Chao Bai Haipeng Ren Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an710048China Xi'an Key Laboratory of Intelligent Equipment Xi'an Technological UniversityXi'an710021China

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting ***,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold *** unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)*** contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using *** proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative *** this approach,the calculation complexity is reduced compared to the previous ESN-based *** proposed method achieves better BER performance compared to the previous *** reason for this superior result is ***,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was ***,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information *** results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

关键词： Chaotic baseband wireless communication system(CBWCS) Inter-symbol interference(ISI) Echo state network(ESN) Threshold prediction

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Data-Driven Analysis:Examining the Impact of COVID-19 from the Perspective of Residential and Non-Residential Electricity Consumption

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Journal of Social Computing 2024年第4期5卷 313-328页

作者： Zhiheng Yang Xiu Cao Qifan Zhang Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200438China

Due to the pandemic’s widespread effects on daily life in the past few years,evaluating and analyzing the impact of COVID-19 on large cities in China accurately and timely has become increasingly *** existing research lacks studies on the electricity consumption of residents and non-residents during the *** based on the four-year electricity consumption data of a large city in China,this paper examines COVID-19’s impact from the perspective of residential and nonresidential electricity consumption through the COVID-19-Power framework and its extended version that predicts electricity consumption in 2020 without the outbreak of COVID-19 for comparison with the actual *** residential electricity consumption,through comparative analysis of different administrative districts on geographic location and historical electricity consumption habits,the impact of the pandemic on residential electricity consumption is found to be relatively *** nonresidential electricity consumption,we infer that power-related factors including real estate development and investment,*** to perception of the special impacts of the *** conduct predictive analysis of non-residential electricity consumption for different administrative districts by geographic location,different electricity consumption *** verify the significant negative impact of the pandemic on the real estate sector by the analysis of electricity consumption across different *** these research results,details about the impact of the pandemic on people’s lives and electrical load in China’s large cities can be captured by power enterprises to adjust power supply strategies more effectively and pertinently.

关键词： COVID-19 social computing electricity consumption analysis forecasting models

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Surface-PMSM Sensorless control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating with Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年第8期40卷 10804-10815页

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors (SPMSMs) due to its strong robustness against measurement and system noise. However, conventional method suffers from poor position and speed estimation accuracy under dynamic conditions, and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this paper. Differing from conventional method, a reduced-order linear state equation encompassing rotor position, speed and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process, but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameters variations. Additionally, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy.1 © 1986-2012 IEEE.

关键词： Permanent magnets

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Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

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Chinese Journal of Electronics 2023年第5期32卷 1106-1119页

作者： DU Haocui XIE Weixin LIU Zongxiang LI Liangqun ATR Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture(TO-MPMBM) filter to address the problem that the standard random finite set filters cannot build continuous trajectories for multiple extended targets. First, the Poisson point process model and the multi-Bernoulli mixture(MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, ***, the proposed filter recursively propagates the marginal association distributions and the Poisson multiBernoulli mixture(PMBM) density over the set of alive trajectories. Finally, after pruning and merging process,the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories. A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.

关键词： Target tracking Uncertainty Monte Carlo methods Trajectory tracking Filtering Merging Filtering algorithms

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A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

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Chinese Journal of Electronics 2023年第3期32卷 429-437页

作者： FAN Jiande XIE Weixin LIU Zongxiang ATR Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multiBernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.

关键词： Image edge detection Clustering algorithms Distributed databases Filtering algorithms information filters Sensors Complexity theory

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A Tabletop Nano-CT Image Noise Reduction Network Based on 3-Dimensional Axial Attention Mechanism

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Computers, Materials & Continua 2024年第7期80卷 1711-1725页

作者： Huijuan Fu Linlin Zhu ChunhuiWang Xiaoqi Xi Yu Han Lei Li Yanmin Sun Bin Yan Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering UniversityZhengzhou450000China

Nano-computed tomography(Nano-CT)is an emerging,high-resolution imaging ***,due to their low-light properties,tabletop Nano-CT has to be scanned under long exposure conditions,which the scanning process is *** 3D reconstruction data,this paper proposed a lightweight 3D noise reduction method for desktop-level Nano-CT called AAD-ResNet(Axial Attention DeNoise ResNet).The network is framed by theU-net *** encoder and decoder are incorporated with the proposed 3D axial attention mechanism and residual dense *** layer of the residual dense block can directly access the features of the previous layer,which reduces the redundancy of parameters and improves the efficiency of network *** 3D axial attention mechanism enhances the correlation between 3D information in the training process and captures the long-distance *** can improve the noise reduction effect and avoid the loss of image structure *** results show that the network can effectively improve the image quality of a 0.1-s exposure scan to a level close to a 3-s exposure,significantly shortening the sample scanning time.

关键词： Deep learning tabletop Nano-CT image denoising 3D axial attention mechanism

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Co-salient object detection with iterative purification and predictive optimization

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虚拟现实与智能硬件（中英文） 2024年第5期6卷 396-407页

作者： Yang WEN Yuhuan WANG Hao WANG Wuzhen SHI Wenming CAO Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related ***,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the *** issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of *** To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD ***,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing *** on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

关键词： Co-salient object detection Saliency detection Iterative method Predictive optimization

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