YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

A New method for selecting multi-scale road network objects

Aimed at solving the problems of road network object selection at any unknown scale, the existing methods on object selection are integrated and extended in this paper, and a new object interpolation method is proposed, which reflects the inheritable and transferable characteristics of related information among multi-scale representation objects, and takes the attribute effects into account. Then the basic idea, the overall framework and the technical flow of the interpolation are put forward, at the samet：me synthetical weight function of the interpolation method is defined and described. The method and technical strategies of object selection are extended, and the key problems are solved, including the dejign of the objective quantitative and structural selections based on the weight values, the interpolation experiment strategies and technical flows, the result of the test shows that the object interpolation method not only inherits the objects at smaller scales, but also takes the attribute effect into account when deriving objects from larger scales according to the road importance, which is a guarantee to objective selection of the road objects at middle scales.

METHOD OF CENTERS ALGORITHM FORMULTI-OBJECTIVE PROGRAMMING PROBLEMS

In this paper, we consider a method of centers for solving multi-objective programming problems, where the objective functions involved are concave functions and the set of feasible points is convex. The algorithm is defined so that the sub-problems that must be solved during its execution may be solved by finite-step procedures. Conditions are given under which the algorithm generates sequences of feasible points and constraint multiplier vectors that have accumulation points satisfying the KKT conditions. Finally, we establish convergence of the proposed method of centers algorithm for solving multiobjective programming problems.

Neighborhood fusion-based hierarchical parallel feature pyramid network for object detection

In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.

面向商业航天卫星成本效益的三模冗余软错误防护技术:近似计算的实践

三模冗余(TMR)作为如今集成电路可靠性领域中最为常用且有效的软错误加固技术,在满足高容错要求之时,不可避免地牺牲了庞大的硬件损耗。为实现面积、功耗等硬件性能和容错电路加固能力的折中考虑,适应低成本高可靠性加固的时代需求,针对基于近似计算的三模冗余加固技术(ATMR)进行研究,该文提出一种基于近似门单元(ApxLib)的动态调整多目标优化框架(ApxLib+DAMOO)。首先,其基本优化框架采用非支配排序遗传算法(NSGA-Ⅱ)实现,通过极性分析与预创建的近似库对电路实现快速近似。随后,该框架提出动态概率调整和极性扩张两种创新机制,根据可测性分析对遗传算法中门单元的突变概率进行动态更新,对双向门单元进行定向识别和重构,以实现寻优效率和寻优效果的双重优化。实验结果表明,该文提出的优化框架与传统NSGA-Ⅱ相比,在相同硬件损耗下可实现最大10%~20%的额外软错误率(SER)降低,且其执行时间平均降低18.7%。

基于多目标优化的箱梁渡槽结构设计研究

针对箱梁渡槽所受水荷载远大于同等跨径箱梁桥所受汽车荷载导致槽身纵向受力突出的问题,在渡槽结构设计中统筹考虑箱梁纵向应力储备和造价,构建了以截面尺寸、钢筋用量等6个参数为设计变量,以渡槽边缘正应力和造价为目标函数,以槽身挠度、法向应力等9个验算指标为约束条件的多目标优化模型,并基于支持向量机近似模型和NSGA2遗传算法求解多目标优化模型的Pareto最优解集,然后综合专家打分、COWA算子、博弈论综合赋权与TOPSIS法从Pareto最优解集中选出最佳方案。结果表明:与初始方案相比,最佳方案的渡槽边缘正应力储备提高了17.55%,渡槽上部结构总造价降低8.84%;与有限元分析相比,该方法在保持较高计算精度的同时节省了65.42%的计算时间成本。

基于灰色关联度的某畜禽车车厢多目标优化

为降低某公司4×2畜禽运输车的生产成本以及提高车辆的燃油经济性,在保证车厢结构性能的前提下,对畜禽车车厢进行轻量化设计。通过对车厢进行静态分析和动态分析,了解其结构性能后,使用混合灵敏度方法从80组设计变量中筛选出30组设计变量,再用哈默斯雷法Hammersley对设计变量进行采样;随后使用移动最小二乘法MLSR创建近似模型,并通过确定系数来判断拟合精度;最后使用多目标遗传算法MOGA对近似模型进行优化并得到帕雷托前沿,并基于灰色关联度分析从该帕雷托前沿中筛选出一组最优板厚。优化后的模型在保证车厢整体结构性能的前提下使车厢减重6.8%,并且一阶模态频率提高2.9 Hz。

某自卸车车厢的多目标尺寸优化

针对某公司的自卸车车厢研究,为了降低车厢的制造成本,故对其进行结构轻量化的研究和探索。使用HyperMesh软件对车厢的三维模型进行有限元建模,并对其有限元模型进行静态和动态特性分析,其结果均满足设计目标,并具有一定的优化空间。但由于车厢设计变量较多,故引入改进灵敏度分析的方法从38组设计变量中筛选出6组关键设计变量。使用哈默斯雷采样法进行200次采样,使用最小二乘法和径向基函数生成响应面搭建近似模型,然后以车厢的应力、位移为约束条件,以质量最小和第6阶模态频率最大为优化目标,使用多目标遗传算法(multi objective genetic algorithm,MOGA)对参数化近似模型进行优化。优化后的结果表明,在保证其车厢静态和动态结构性能的同时,最终优化结果减重0.705 t,减重率10.34%,减重效果明显。

空间机器人的同伦类约束路径规划

同伦类约束路径规划是空间机器人路径规划的一种重要技术手段,能够有效实现复杂约束条件下路径规划的运算轻量化,具有重要的理论意义和应用价值。同伦路径规划以同伦这一数学工具作为基础,将空间机器人的可行路径集限制在范围有限且具有良好函数性质的同伦结构中,在该结构中进行优化路径的搜索,在保证路径规划有效性的同时降低了算法的复杂性。介绍了基于同伦的路径规划近似算法框架,以及路径同伦构造和规划问题的求解。从面向约束的路径同伦构造方法和同伦优化搜索2个层面分析了近年来针对空间机器人同伦路径规划研究的进展。根据研究现状,对空间机器人的同伦类约束路径规划的未来发展进行了展望。

助推滑翔变体飞行器弹道方案多目标近似优化

针对助推滑翔变体飞行器弹道方案最优变形求解难、多设计指标相互矛盾等问题,开展助推滑翔变体飞行器弹道方案多目标设计优化研究。首先构建了助推滑翔变体飞行器全程弹道方案优化框架,通过内外层分别优化控制参数及弹道方案参数,并建立了以起飞质量最小、射程最大为优化目标的弹道方案多目标优化模型。在弹道建模中,基于牛顿迭代法建立助推段弹道模型,基于伪谱法建立最优变体再入滑翔段弹道模型。此外,提出了基于差分进化的多目标近似约束优化方法(MACO-DE),实现助推滑翔变体飞行器弹道方案优化。对比初始方案,在射程不变情况下,起飞质量至多降低3.81%,在起飞质量不变情况下,射程至多提升6.62%,从而验证了全程弹道模型的合理性与MACO-DE方法的有效性。

基于协同优化的耐压柱壳多学科多目标优化

为提高耐压柱壳的设计效率和结构性能,本文基于多学科设计优化思想,进行了耐压柱壳多学科设计优化的学科分解,确定了耐压柱壳多学科优化设计的设计参数,明确了设计变量间的耦合关系,设计了耐压柱壳参数化分析流程,进行了设计变量灵敏度分析。在利用响应面模型建立了具有较高拟合精度耐压柱壳近似模型的基础上,以质量排水量比和极限载荷为目标函数,建立了基于协同优化方法的耐压柱壳多学科多目标优化模型,在确定了系统层和学科层的优化求解方法基础上,设计了基于协同优化的耐压柱壳多学科多目标优化框架,进行了优化的求解工作,得到了优化方案。与初始方案的对比表明:所得到的Pareto解集中,部分方案在2个目标函数上均得到了优化,验证了优化框架的有效性。

言语数词习得的认知基础

儿童从学前期开始学习言语数词(如理解数词的数量含义),但其认知基础为何尚有很大争议。现有研究聚焦检查儿童的数词概念是否建立于自婴儿期就具有的非言语数能力基础上。考虑到非言语数能力受到至少两个不同的数认知系统的支持(近似数量系统和物体追踪系统),研究者检查数词概念习得是否依赖于这些数认知系统,并发展出四种假设:近似数量系统假设,物体追踪系统假设,双系统假设和独立假设。这些假设均有其合理性和局限性,检验四种假设的关键是同时纳入两个非言语数认知系统并对比它们对数词习得的贡献。在这个检验思路中,未来研究需提供追踪和干预训练数据检查两个非言语数认知系统和数词习得的关系,且将之在行为和大脑认知神经层面交叉印证;此外,对这些关系的检查还需注意区分儿童数词学习的不同阶段,并考虑其他一般性认知能力的影响。

定向距离函数的光滑化方法及其应用

本文考虑定向距离函数的光滑化表示及其应用。首先在已有的两种光滑化方法的基础上,给出了这类特殊的非光滑函数的光滑化表示。作为特例,在二维空间中,给出该函数更具体的光滑化函数。最后利用定向距离函数的光滑化函数以及它在多目标优化问题标量化方法中的应用,建立非光滑多目标优化问题的光滑标量化模型,并给出了两者之间解集的关系。

Modification to first-order Born approximation for improved prediction of scattered sound from weakly scattering objects

When deriving the Fourier diffraction theorem based on the first-order Born approximation,the difference between wave number of the scattering object and that of the surrounding medium is ignored,causing substantial errors in sound scattering prediction.This paper modifies the Born approximation by taking into account the amplitude and phase changes between the scattering object and the water due to the wave number difference.By changing the radius and center position of the sampling circle in the Fourier domain,accuracy of the predicted sound scattering is improved.With the modified Born approximation,the computed far-field directional pattern of the scattered sound from a circular cylinder is in good agreement with the rigorous solution.Numerical calculations for several objects with different shapes are used to show applicability and effectiveness of the proposed method.

铸铝一体化车门的多目标可靠性优化设计

为提升车门的轻量化水平与性能,采用“材料—结构—性能”一体化集成方法设计铸铝一体化车门。基于构建的铸铝一体化车门有限元模型,以车门的厚度为设计变量,采用径向基函数(radial basis function,RBF)神经网络近似模型和二阶响应面近似模型并分别结合二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)、多目标粒子群优化(multi-objective particle swarm optimization,MOPSO)算法以及多岛遗传算法(multi-island genetic algorithm,MIGA)对车门的下沉刚度工况位移、上扭转刚度工况位移、下扭转刚度工况位移、一阶弯曲模态频率、一阶扭转模态频率和质量进行确定性优化设计。在此基础上,考虑材料及加工制造等不确定性因素,对确定性优化解的质量水平进行6Sigma可靠性分析与优化。结果表明,二阶响应面近似模型与MOPSO算法的优化组合方案实现了车门的最佳轻量化,RBF神经网络近似模型与MOPSO算法的优化组合方案实现了车门下沉刚度工况位移的最小化。上述2种组合分别实现了车门轻量化与安全化的设计目标。研究结果可为车身零部件的优化设计提供参考。

Modeling effectiveness and identification of multi-scale objects in farmland soils with improved Yee-FDTD methods

Finite-Difference Time-Domain(FDTD)is the most popular time-domain approach in computational electromagnetics.Due to the Courant-Friedrich-Levy(CFL)condition and the perfect match layer(PML)boundary precision,FDTD cannot simulate soil medium whose surface is connected by multiple straight lines or curves(multi-scale)accurately and efficiently,which greatly limits the application of FDTD method to simulate buried objects in soils.Firstly,this study proposed the absorption boundary and adopted two typical perfect matching layers(UPML and CPML)to compare their absorption effects,and then using the three forms of improved Yee-FDTD algorithm,alternating-direction implicit(ADI-FDTD),unconditionally stable(US-FDTD)and hybrid implicit explicit finite time-domain(HIE-FDTD)to divide and contrast the boundary model effects.It showed that the HIE-FDTD was suitable for inversion of multi-scale structure object modeling,while ADI-FDTD and US-FDTD were ideal for single-boundary objects in both uniaxial perfectly matched layer(UMPL)and convolution perfectly matched layer(CPML)finite element space.After that,all the models were tested by computer performance for their simulated efficiency.When simulating single boundary objects,UPML-US-FDTD and ADI-FDTD could achieve the ideal results,and in the boundary inversion of multi-scale objects,HIE-FDTD modeling results and efficiency were the best.Test modeling speeds of CPML-HIE-FDTD were compared with three kinds of waveform sources,Ricker,Blackman-Harris and Gaussian.Finally,under the computer condition in which the CPU was i5-8250,the HIE-FDTD model still had better performance than the traditional Yee-FDTD forward modeling algorithm.For modeling multi-scale objects in farmland soils,the methods used CPML combined with the HIE-FDTD were the most efficient and accurate ways.This study can solve the problem that the traditional FDTD algorithm cannot construct non-mesh objects by utilizing the diversity characteristics of Yee cell elements.

基于SPEA-Ⅱ算法的网络多层次安全访问控制方法

当前网络技术迅猛发展,网络安全问题日益突出,尤其是访问控制安全性;为了解决目前网络系统中存在的威胁检测精度和安全访问控制问题,引入SPEA-Ⅱ算法,提出一种新的网络多层次安全访问控制方法;深入分析网络多层次访问控制机制原理,明确安全访问控制目标;结合网络边界区域、传输信道区域及移动终端设备区域,建立边界访问控制目标函数、安全威胁检测目标函数、用户身份认证目标函数,提高网络系统安全威胁检测精度;利用SPEA-Ⅱ算法对联合目标函数进行求解,获取网络多层次安全访问控制机制最佳方案;通过迭代逐渐接近近似最优解集,建立最优安全策略组合,计算攻击流量因子完成网络多层次安全访问控制;实验结果表明,所提方法的网络多层次威胁检测精度为94%,安全评估为0.96;由此证明,所提方法的网络多层次安全访问控制效果较好,具有较强的安全性和适应性,能够为中移互联网领域的网络安全策略提供技术支持。

机床床身结构优化设计方法

结合基于近似模型的尺寸优化及拓扑优化技术,提出了机床床身结构优化设计的策略。首先以床身结构的刚度和基频为设计目标,以床身内部横隔板间距及厚度、垫铁间距为设计变量,通过中心复合设计法生成实验所需的设计变量的样本点,对生成的样本点进行有限元分析得到样本点的响应,借助最小二乘法建立近似模型二阶响应曲面。采用折衷法建立多目标优化模型,并用遗传算法进行求解。在此基础上,采用密度法对床身结构进行拓扑优化。实例计算结果表明,优化后床身质量减轻了4.45%,而刚度提高了5.8%。

响应面模型与多目标遗传算法相结合的机床立柱参数优化

针对传统方法在复杂机械结构优化设计中的低效性和局限性,提出了一种将响应面模型与多目标遗传算法相结合的优化设计方法.采用拉丁超立方试验的设计方法,在设计空间抽取样本点并进行数值模拟,建立了由7个设计参数所决定的立柱最大变形、首阶固有频率及质量的初始二阶响应面模型.利用多目标遗传算法对响应面模型进行循环逼近优化,得到了Pareto最优解集.仿真结果表明,在质量不变的条件下,优化解可使立柱首阶固有频率增加15.9%,导轨处最大变形减小7.7%,可通过有限次数的数值模拟计算获得设计空间内的近似最优解,因此较适用于机床及类似复杂结构的多目标多参数的优化设计.