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Multiscale feature learning and attention mechanism for infrared and visible image fusion

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Science China(Technological Sciences) 2024年第2期67卷 408-422页

作者： GAO Li luo delin WANG Song School of Aerospace Engineering Xiamen UniversityXiamen 361102China Electric Power Research Institute China Southern Power GridGuangzhou 510063China

Current fusion methods for infrared and visible images tend to extract features at a single scale,which results in insufficient detail and incomplete feature preservation.To address these issues,we propose an infrared and visible image fusion network based on a multiscale feature learning and attention mechanism(MsAFusion).A multiscale dilation convolution framework is employed to capture image features across various scales and broaden the perceptual scope.Furthermore,an attention network is introduced to enhance the focus on salient targets in infrared images and detailed textures in visible images.To compensate for information loss during convolution,jump connections are utilized during the image reconstruction phase.The fusion process utilizes a combined loss function consisting of pixel loss and gradient loss for unsupervised fusion of infrared and visible images.Extensive experiments on the dataset of electricity facilities demonstrate that our proposed method outperforms nine state-of-theart methods in terms of visual perception and four objective evaluation metrics.

关键词： infrared and visible images image fusion attention mechanism CNN feature extraction

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

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Science China(Technological Sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang ZHANG QieShi CHENG Qin XU ZhenYu YUAN Shuai luo delin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace Engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive topic.However,one of the main challenges is to effectively extract complementary features from different modalities for action recognition.In this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action recognition.Built on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation features.We evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and PKU-MMD.The results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

UAV swarm formation reconfiguration control based on variable-stepsize MPC-APCMPIO algorithm

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Science China(Information Sciences) 2023年第11期66卷 222-243页

作者： Jian LIAO Jun CHENG Bin XIN delin luo Lihui ZHENG Yuhang KANG Shaolei ZHOU School of Physics and Electronic Information Gannan Normal University Shenzhen Institute of Advanced Technology Chinese Academy of Sciences School of Automation Beijing Institute of Technology School of Aerospace Engineering Xiamen University School of Electronic Information Northwest University of Technology 6. School of Basic Science Naval Aviation University of PLA

For a complex operational environment, to actualize safe obstacle avoidance and collision avoidance, a swarm must be capable of autonomous formation reconfiguration. First, this paper introduces the basic pigeon-inspired optimization(PIO) algorithm, and establishes the unmanned aerial vehicle motion model and the virtual leader swarm formation control structure. Then, given the above knowledge, the basic error objective function of a UAV swarm, obstacle avoidance objective function, and collision avoidance objective function are devised based on the variable-stepsize model predictive control technique. Next,the adaptive perception Cauchy mutation PIO algorithm is proposed by introducing the Cauchy mutation operator, adaptive weight factor, and roulette wheel selection into the basic PIO. This algorithm is used to optimally solve the abovementioned swarm objective functions. Ultimately, a set of comparative simulations are performed to verify the effectiveness and reliability of the proposed algorithm.

关键词： unmanned aerial vehicle swarm formation reconfiguration variable-stepsize model predictive control (MPC) adaptive perception Cauchy mutation pigeon-inspired optimization (APCMPIO) obstacle avoidance

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未知干扰下四旋翼无人机群固定时间编队控制

北京航空航天大学学报 2024年第5期50卷 1702-1712页

作者：郑伟铭徐扬罗德林厦门大学航空航天学院厦门361102 西北工业大学民航学院西安710072 西北工业大学太仓长三角研究院苏州215400

在未知干扰条件下,针对欠驱动、非线性的四旋翼无人机群编队控制问题,提出了一种抗干扰的固定时间编队控制方法。首先,设计分布式固定时间滑模估计器,在仅有部分无人机获取到期望轨迹的条件下,每架无人机都能快速估计出自己的期望位置... 详细信息

在未知干扰条件下,针对欠驱动、非线性的四旋翼无人机群编队控制问题,提出了一种抗干扰的固定时间编队控制方法。首先,设计分布式固定时间滑模估计器,在仅有部分无人机获取到期望轨迹的条件下,每架无人机都能快速估计出自己的期望位置信息。其次,引入固定时间干扰观测器用于估计未知的复合干扰,使无人机能够生成干扰估计值以抵消复合干扰对其带来的影响。然后,在无人机的位置层与控制层设计固定时间滑模控制器,实现对期望轨迹的跟踪,所给出的方法能够满足姿态稳定收敛。最后,数值仿真验证了所提出方法的有效性。

关键词：四旋翼无人机欠驱动系统编队控制固定时间控制干扰观测器

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

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International Journal of Intelligent Computing and Cybernetics 2018年第2期11卷 166-180页

作者： Long Xin delin luo Han Li School of Automation Science and Electrical Engineering Beijing University of Aeronautics and AstronauticsBeijingChina School of Aerospace Engineering Xiamen UniversityXiamenChina

Purpose–The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling(AAR)for unmanned aerial vehicle,which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approach–Methods and techniques for marker detection,feature matching and pose estimation have been designed and implemented in the visual measurement system.Findings–The simple blob detection(SBD)method is adopted,which outperforms the Laplacian of Gaussian method.And a novel noise-elimination algorithm is proposed for excluding the noise points.Besides,a novel feature matching algorithm based on perspective transformation is proposed.Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implications–The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/value–The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed.Besides,a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

关键词： Unmanned aerial vehicle(UAV) Autonomous aerial refueling(AAR) Computer vision Pose estimation Random sample consensus(RANSAC)

Docking navigation method for UAV autonomous aerial refueling

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Science China(Information Sciences) 2019年第1期62卷 29-44页

作者： delin luo Jiang SHAO Yang XU Jinpeng ZHANG School of Aerospace Engineering Xiamen University China Airborne Missile Academy Aviation Key Laboratory of Science Technology on Airborne Guided Weapon

In this paper, a docking navigation method for autonomous aerial refueling(AAR) of unmanned aerial vehicles(UAVs) based on a binocular vision system(BVS) is proposed. A BVS simulation platform is built for simulation research purposes. First, unnecessary scene information in the image is filtered through green light-emitting diodes(LEDs) and filters. Then the image is processed via graying, binarization, and median filtering to highlight the connected area of the LED in the image. Subsequently, the center of mass of the connected area is selected as the feature point(FP), and the FPs are described using an improved Haar wavelet transform. The multidimensional description vector of FP is obtained and matched. Finally,the position and pose of the refueling cone sleeve are estimated. Simulation results show the effectiveness of the presented AAR navigation method.

关键词： autonomous aerial refueling AAR position and pose estimation improved Haar wavelet transform IHWT binocular vision system BVS BVS simulation platform

Target-enclosing affine formation control of two-layer networked spacecraft with collision avoidance

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Chinese Journal of Aeronautics 2019年第12期32卷 2679-2693页

作者： Yang XU delin luo Dongyu LI Yancheng YOU Haibin DUAN School of Aerospace Engineering Xiamen UniversityXiamen 361005China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.

关键词： Affine formation control Collision avoidance Lyapunov stability Target enclosing Two-layer strategy

A Continuous Time Domain Direct Identication Method for Unsteady Fluid Dynamic Model in Incompressible Flow

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Guidance, Navigation and Control 2023年第1期3卷 85-116页

作者： Xianwu Lin Tao Li Lixun Xiong delin luo School of Aerospace Engineering Xiamen UniversityXiang an South RoadXiamenFujian Province 361102P.R.China

To identify the unsteady°uid dynamic model for a solid moving in the°uid,a new identication method that operates in the continuous time domain is proposed.It is illustrated that this new identication method is more accurate and appropriate for identifying unsteady°uid dynamic model in incompressible°ow in comparison with the classical frequency domain or discrete domain method.This new method is applied to identify the unsteady°uid dynamic models of a NACA0012 airfoil and the e®ect of the°uid viscosity on the model parameter is analyzed.The result shows that the added mass in viscous°ow is dependent on the input signal frequency and is exactly the added mass in inviscid°ow when the frequency approaches innity.Based on this discovery,the long controversy on the relationship of added mass between viscous and inviscid°ow is solved.Then,the enlightenment of this discovery on the identication of both linear and nonlinear unsteady aerodynamic model in incompressible°ow is illustrated.At last,the advantage of the new acquired unsteady°uid dynamic model is discussed in comparison with the classical quasi-steady aerodynamic model.

关键词： Model identication unsteady fluid dynamics added mass non-causal system.

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Guest editorial

International Journal of Intelligent Computing and Cybernetics 2017年第4期10卷 410-411页

作者： delin luo Feng Lin Department of Automation Xiamen UniversityXiamenChina Temasek Laboratories National University of SingaporeSingapore

Autonomous control of Unmanned Aerial System(UAS)is that UASs can make decisions in the flight and carry out missions autonomously according to the scheduled tasks and rules through online sensing the surrounding situation.Through cooperation,UASs can share information among individual agents and work together to accomplish complicated mission which would not be possible for a single agent otherwise.With the development of UAS technology,autonomous control has become one of the hot topics and key technologies in UAS research field.

关键词： agent individual autonomous