航空遥感图像深度学习目标检测技术研究进展

航空遥感图像目标检测旨在定位和识别遥感图像中感兴趣的目标,是航空遥感图像智能解译的关键技术,在情报侦察、灾害救援和资源勘探等领域具有重要应用价值。然而由于航空遥感图像具有尺寸大、目标小且密集、目标呈任意角度分布、目标易被遮挡、目标类别不均衡以及背景复杂等诸多特点,航空遥感图像目标检测目前仍然是极具挑战的任务。基于深度卷积神经网络的航空遥感图像目标检测方法因具有精度高、处理速度快等优点,受到了越来越多的关注。为推进基于深度学习的航空遥感图像目标检测技术的发展,本文对当前主流遥感图像目标检测方法,特别是2020—2022年提出的检测方法,进行了系统梳理和总结。首先梳理了基于深度学习目标检测方法的研究发展演化过程,然后对基于卷积神经网络和基于Transformer目标检测方法中的代表性算法进行分析总结,再后针对不同遥感图象应用场景的改进方法思路进行归纳,分析了典型算法的思路和特点,介绍了现有的公开航空遥感图像目标检测数据集,给出了典型算法的实验比较结果,最后给出现阶段航空遥感图像目标检测研究中所存在的问题,并对未来研究及发展趋势进行了展望。     

基于深度学习的实例分割研究进展

目标检测确定检测图像中目标对象所在区域及其类别,语义分割对检测图像实现像素级分类,实例分割可以定义为同时解决目标检测与语义分割问题,在分类的同时确定每个目标实例语义。实例分割网络在无人机驾驶、机器人抓取、工业筛检等领域具有重要应用意义,针对目前基于深度学习实例分割综述性文章的空白,对实例分割进展进行概述,按照单阶段实例分割与双阶段实例分割的分类对不同网络模型进行论述,重点介绍近两年网络框架的发展,总结各网络特点的同时提出未来发展方向。     

基于改进Faster RCNN与Grabcut的商品图像检测

近年来,图像检测方法已经被应用于很多领域.然而,这些方法都需要在目标任务上进行大量边框标注数据的重新训练.本文基于Faster RCNN方法,并对其进行改进,解决了在小数据且无需边框标注的情况下的商品图像检测问题.首先对Faster RCNN的边框回归层进行改进,提出了一种非类别特异性的边框回归层,仅使用公开数据集训练,无需在目标数据集上进行再训练,并将其用于数据预标定与商品检测.然后结合Grabcut与非类别特异性Faster RCNN提出了一种样本增强方法,用来生成包含多个商品的训练图像;并为Faster RCNN添加了重识别层,提高了检测精度.     

可见光图像人造目标检测技术综述

对可见光自然图像人造目标检测技术研究进行综述,在人造目标检测技术的问题描述基础上,基于人造目标与自然背景存在的特性差异,将现有技术按照基于几何特征、分形特征、概率模型、水平集、聚类等类别进行了讨论,并借鉴ATR（自动目标识别）技术评价标准分析了各个算法的优缺点。最后探讨了人造目标检测技术存在的问题和进一步的研究方向。     

基于红外相机和毫米波雷达融合的夜间行人检测

为了提升夜间环境行人检测的能力,使用红外相机和毫米波雷达进行信息融合.对两传感器数据进行时间配准并分别进行处理,利用改进YOLO算法处理红外图像得到目标类别特征,处理毫米波雷达数据获得目标的距离和速度特征,再建立空间对准模型对两传感器进行目标匹配,最后利用基于特征的融合算法完成夜间行人多模态信息输出.两传感器检测目标首次匹配之后,可以将类别信息反馈给雷达数据处理单元进行记录,当红外图像检测算法漏检时,利用雷达记录的信息补充输出类别特征.通过实验证明该融合算法提升了单一传感器行人检测成功率,在夜间场景具有良好的应用效果.     

深度学习的人-物体交互检测研究进展

人-物体交互检测(HOI),就是把图像作为输入,检测出图像中存在交互行为的人和物体以及他们之间的交互动词。它是计算机视觉范畴里继目标检测、图像分割和目标跟踪之后又一新任务,旨在对图像进行更深层的理解。针对目前基于深度学习的HOI检测综述性文章的空白,以HOI检测方法的发展历程为主线,对基于深度学习的HOI检测方法进行了分类与分析。首先简要总结了早期的技术方法,然后根据模型结构将现有算法分为两阶段方法和一阶段方法并对一些代表性算法进行分析介绍。将两阶段方法分为融入注意力、图模型以及姿势和身体部位三类进行重点论述,总结了每类方法的基本思想与优缺点。此外,还详细介绍了HOI检测任务的实验评价指标、基准数据集和大多数现有方法的实验结果,对不同类别的方法取得的结果进行说明。最后对该技术面临的主要挑战进行总结分析并对未来发展趋势进行展望。     

深度学习在目标视觉检测中的应用进展与展望

目标视觉检测是计算机视觉领域的一个重要问题,在视频监控、自主驾驶、人机交互等方面具有重要的研究意义和应用价值.近年来,深度学习在图像分类研究中取得了突破性进展,也带动着目标视觉检测取得突飞猛进的发展.本文综述了深度学习在目标视觉检测中的应用进展与展望.首先对目标视觉检测的基本流程进行总结,并介绍了目标视觉检测研究常用的公共数据集;然后重点介绍了目前发展迅猛的深度学习方法在目标视觉检测中的最新应用进展;最后讨论了深度学习方法应用于目标视觉检测时存在的困难和挑战,并对今后的发展趋势进行展望.     

CNN和Transformer在细粒度图像识别中的应用综述

细粒度图像识别旨在从类别图像中辨别子类别。由于图像间只有细微差异,这使得识别任务具有挑战性。随着深度学习技术的不断进步,基于深度学习的方法定位局部和表示特征的能力越来越强,其中以卷积神经网络（CNN）和Transformer为基础的各类算法大大提高了细粒度图像识别精度,细粒度图像领域得到了显著发展。为了整理两类方法在细粒度图像识别领域的发展历程,对该领域近年来只运用类别标签的方法进行了综述。介绍了细粒度图像识别的概念,详细阐述了主流细粒度图像数据集;介绍了基于CNN和Transformer的细粒度图像识别方法及其性能;最后,总结了细粒度图像识别未来的研究方向。     

基于增量学习的目标检测研究

目标检测技术已经被广泛应用于行人检测、人脸识别等诸多领域。随着社会生活与工业发展中新需求的出现,目标检测的对象与要求也随之出现新的变化。若直接用旧模型训练新类别可能会导致灾难性遗忘缺陷。因此,增量目标检测逐渐成为一个热门的研究方向。总结了常用的数据集和模型评价指标,研究了增量目标检测技术,将增量目标检测分为基于知识蒸馏的目标检测模型、基于回放的增量目标检测模型、开放世界目标检测模型,指出目前增量目标检测存在新增可识别类别的数据有限、新的可识别类别增加会导致模型准确率降低、小目标检测困难、检测速度慢等问题。通过比较最新增量目标检测模型,提出未来增量目标检测应从优化知识蒸馏方式、加强旧目标类别范例样本选择、更好地结合Transformer网络等几个方面进行改进。     

基于卷积神经网络和语义相关的协同显著性检测

针对目前协同显著性检测方法中存在的语义特征类相差悬殊的物体被误检测为协同对象等问题,提出了一种基于卷积神经网络和语义相关的协同显著性检测算法CSCCD。首先,采用引导超像素滤波方法对SLIC分割出的超像素区域和DSS生成的显著性区域进行处理,清晰地显示了目标边界轮廓;然后使用Mask R-CNN提取语义特征,给出了图像语义特征和语义一致性的定义,并针对提取语义特征过程中出现的同一语义类别的物体在不同形态下被检测为不同语义类别的问题,提出了图像组语义相关类的概念,在此概念的基础上定义了图像组语义关联类,解决了多幅图像的语义关联问题;最后融合显著性检测区域和图像组语义一致性区域得到协同显著性检测结果。在公开基准数据集上的实验结果表明,该算法能够有效凸显目标整体及轮廓,在客观量化方面的综合性能有明显提升。     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).