多特征分类的PolSAR图像飞机目标检测方法

针对目前有关极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)的飞机目标检测算法虚警较多、自适应性较差的问题,给出一种复杂大场景中PolSAR图像多特征分类的飞机目标检测方法。该方法分为线下分类器训练和飞机目标检测两部分。使用Filter特征选择结合穷举法筛选出分类性能高的飞机极化特征训练SVM (Support Vector Machine, SVM)分类器;利用异化散射功率提取疑似飞机目标,进一步提取多个极化特征送入SVM分类获得检测结果。利用UAVSAR系统采集的多幅实测数据进行实验,并与现有的PolSAR图像飞机目标检测算法进行对比,结果表明该方法能够有效检测出飞机目标,并且虚警和漏警较少,方法自适应性有所提高。      

先验引导的特征金字塔阴影检测网络

阴影检测向来是计算机视觉领域的一个基础性挑战。它需要网络理解图像的全局语义和局部细节信息。本文提出了一种检测阴影区域的先验特征金字塔网络结构。该网络搭建了先验加权模块来提取图像中蕴含的阴影先验信息,通过使用阴影先验信息加权卷积特征,引导网络学习到阴影区域。同时,该网络还应用了特征融合模块来融合粗略的语义信息和自上而下路径中的精细特征,并且加入了后处理,进一步优化网络的预测结果。本文在两个公开的阴影检测基准数据集上进行了实验来评估其网络性能。实验表明,本文的方法能够更准确地检测到阴影,和过去最先进的方法相比也表现出色,在SBU数据集上正确率达到了96.6%,平衡检测错误因子为6.22。      

基于Word Embedding的遥感影像检测分割

遥感影像检测分割技术通常需提取影像特征并通过深度学习算法挖掘影像的深层特征来实现.然而传统特征（如颜色特征、纹理特征、空间关系特征等）不能充分描述影像语义信息,而单一结构或串联算法无法充分挖掘影像的深层特征和上下文语义信息.针对上述问题,本文通过词嵌入将空间关系特征映射成实数密集向量,与颜色、纹理特征的结合.其次,本文构建基于注意力机制下图卷积网络和独立循环神经网络的遥感影像检测分割并联算法（Attention Graph Convolution Networks and Independently Recurrent Neural Network,ATGIR）.该算法首先通过注意力机制对结合后的特征进行概率权重分配;然后利用图卷积网络（GCNs）算法对高权重的特征进一步挖掘并生成方向标签,同时使用独立循环神经网络（IndRNN）算法挖掘影像特征中的上下文信息,最后用Sigmoid分类器完成影像检测分割任务.以胡杨林遥感影像检测分割任务为例,我们验证了提出的特征提取方法和ATGIR算法能有效提升胡杨林检测分割任务的性能.     

基于深度阴影特征增强的任意至任意重光照

任意至任意重光照利用隐含在引导图像中的光照重新照明源图像。现有的任意至任意重光照方法由于采用端到端的学习方式,导致阴影特征与色温特征高度耦合,进一步影响了阴影生成的准确性。为此,本文提出了一个基于深度阴影特征增强的任意至任意重光照方法。该方法的关键是设计一个额外的阴影解码器,从隐式表征中直接生成对应的阴影图像。同时,为了充分利用学习到的阴影特征,我们引入一个基于注意力机制的特征融合模块,实现重光照特征与阴影特征的自适应融合。另外,我们实验性地发现,利用多项式核函数把源图像映射到高维特征后,再作为网络输入,能进一步提升性能。在VIDIT数据集上的实验表明了本文所提方法的有效性。      

YOLO-NKLT视觉SLAM回环检测方法

针对存在明显光照变化或遮挡物等室外复杂场景下,现有基于深度学习的视觉即时定位与地图构建(visual simultaneous localization and mapping,视觉SLAM)回环检测方法没有很好地利用图像的语义信息、场景细节且实时性差等问题,本文提出了一种YOLO-NKLT视觉SLAM回环检测方法。采用改进损失函数的YOLOv5网络模型获取具有语义信息的图像特征,构建训练集,对网络重训练,使提取的特征更加适用于复杂场景下的回环检测。为了进一步提高闭环检测的实时性,提出了一种基于非支配排序的KLT降维方法。通过在New College数据集和光照等变化更复杂的Nordland数据集上进行实验,结果表明：室外复杂场景下,相较于其他传统和基于深度学习的方法,所提方法具有更高的鲁棒性,可以取得更佳的准确率和实时性表现。     

全卷积神经网络应用于SAR目标检测

在合成孔径雷达（SAR）图像目标检测中,由于场景杂波的复杂多变,对背景杂波统计模型估计难度增加,从而导致多数检测器容易受到背景杂波的干扰。针对如何避免场景杂波对目标检测干扰的问题,提出了一种基于全卷积神经网络的SAR目标检测模型。该模型将目标检测任务转化为像素分类问题,利用卷积神经网络对数据集中目标像素特征和背景杂波像素的先验信息进行自主学习,有效减少了虚警目标的数量;通过对目标及其阴影区域的联合检测,提高了目标的检测概率。对多个不同场景图像进行测试,实验结果表明提出的检测模型具有良好的检测性能和鲁棒性能,与传统恒虚警检测算法相比,在无需考虑背景杂波统计模型前提下有效降低了虚警概率。     

基于二维道路图像理解算法的阴影检测

提出了一种新的自动检测二维彩色道路场景图像阴影区域的方法。该方法首先从RGB色彩空间中提取出亮度信息,并进行图像分割,再结合从色差图像中提取的阴影信息,检测并消除可能存在的阴影区域,减小在直方图分割中由于阴影的影响而引起的分割误差,最终达到正确的图像分割结果。实验结果表明,该方法简便且有效,并能适应各种不同的二维道路场景需求,具有一定的鲁棒性。     

Shadow detection via multi-scale feature fusion and unsupervised domain adaptation

Shadow detection is significant for scene understanding. As a common scenario, soft shadows have more ambiguous boundaries than hard shadows. However, they are rarely present in the available benchmarks since annotating for them is time-consuming and needs expert help. This paper discusses how to transfer the shadow detection capability from available shadow data to soft shadow data and proposes a novel shadow detection framework (MUSD) based on multi-scale feature fusion and unsupervised domain adaptation. Firstly, we set the existing labeled shadow dataset (i.e., SBU) as the source domain and collect an unlabeled soft shadow dataset (SSD) as the target domain to formulate an unsupervised domain adaptation problem. Next, we design an efficient shadow detection network based on the double attention module and multi-scale feature fusion. Then, we use the global–local feature alignment strategy to align the task-related feature distributions between the source and target domains. This allows us to obtain a robust model and achieve domain adaptation effectively. Extensive experimental results show that our method can detect soft shadows more accurately than existing state-of-the-art methods.     

SDRNet: An end-to-end shadow detection and removal network

Image shadow detection and removal can effectively recover image information lost in the image due to the existence of shadows, which helps improve the accuracy of object detection, segmentation and tracking. Thus, aiming at the problem of the scale of the shadow in the image, and the inconsistency of the shadowed area with the original non-shadowed area after the shadow is removed, the multi-scale and global feature (MSGF) is used in the proposed method, combined with the non-local network and dense dilated convolution pyramid pooling network. Besides, aiming at the problem of inaccurate detection of weak shadows and complicated shape shadows in existing methods, the direction feature (DF) module is adopted to enhance the features of the shadow areas, thereby improving shadow segmentation accuracy. Based on the above two methods, an end-to-end shadow detection and removal network SDRNet is proposed. SDRNet completes the task of sharing two feature heights in a unified network without adding additional calculations. Experimental results on the two public datasets ISDT and SBU demonstrate that the proposed method achieves more than 10% improvement in the BER index for shadow detection and the RMSE index for shadow removal, which proves that the proposed SDRNet based on the MSGF module and DF module can achieve the best results compared with other existing methods.     

Semantic loop closure detection based on graph matching in multi-objects scenes

Robust loop-closure detection is essential for visual SLAM. Traditional methods often focus on the geometric and visual features in most scenes but ignore the semantic information provided by objects. Based on this consideration, we present a strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection. To align two sub-graphs efficiently, we use a sparse Kuhn–Munkres algorithm to speed up the search for correspondence among nodes. The shape similarity and the Euclidean distance between objects in the 3-D space are leveraged unitedly to measure the image similarity through graph matching. Furthermore, the proposed approach has been analyzed and compared with the state-of-the-art algorithms at several datasets as well as two indoor real scenes, where the results indicate that our semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision.     

基于统计的灰度视频自适应背景建模算法

针对灰度视频的目标检测依赖先验知识、召回率低以及单一算法无法同时兼顾静态与动态背景等问题,提出一种基于统计的背景建模算法。该算法无需先验知识,根据统计信息可以准确区分静态背景和动态背景,并采取不同的检测策略提取目标。对于静态背景,采用改进的三帧差分法自适应设置阈值,可以保证较高的召回率。对于动态背景,采用改进的概率密度估计法可以有效降低虚警率。采用所提算法对光照变化以及阴影进行处理,可以进一步提升算法的性能。在公开数据集与实际采集红外数据进行验证实验。实验结果表明,所提算法在多种场景中处理灰度视频的结果比其他传统算法好,在保证准确率的同时可以极大地提升召回率,并且有效提高目标的完整性。     

Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

Target detection in remote sensing images (RSIs) is a fundamental yet challenging problem faced for remote sensing images analysis. More recently, weakly supervised learning, in which training sets require only binary labels indicating whether an image contains the object or not, has attracted considerable attention owing to its obvious advantages such as alleviating the tedious and time consuming work of human annotation. Inspired by its impressive success in computer vision field, in this paper, we propose a novel and effective framework for weakly supervised target detection in RSIs based on transferred deep features and negative bootstrapping. On one hand, to effectively mine information from RSIs and improve the performance of target detection, we develop a transferred deep model to extract high-level features from RSIs, which can be achieved by pre-training a convolutional neural network model on a large-scale annotated dataset (e.g. ImageNet) and then transferring it to our task by domain-specifically fine-tuning it on RSI datasets. On the other hand, we integrate negative bootstrapping scheme into detector training process to make the detector converge more stably and faster by exploiting the most discriminative training samples. Comprehensive evaluations on three RSI datasets and comparisons with state-of-the-art weakly supervised target detection approaches demonstrate the effectiveness and superiority of the proposed method.     

基于深度帧差卷积神经网络的运动目标检测方法研究

复杂场景中的运动目标检测是计算机视觉领域的重要问题,其检测准确度仍然是一大挑战.本文提出并设计了一种用于复杂场景中运动目标检测的深度帧差卷积神经网络（Deep Difference Convolutional Neural Network,DFDCNN）.DFDCNN由DifferenceNet和AppearanceNet组成,不需要后处理就可以预测分割前景像素.DifferenceNet具有孪生Encoder-Decoder结构,用于学习两个连续帧之间的变化,从输入（t帧和t+1帧）中获取时序信息;AppearanceNet用于从输入（t帧）中提取空间信息,并与时序信息融合;同时,通过多尺度特征图融合和逐步上采样来保留多尺度空间信息,以提高网络对小目标的敏感性.在公开标准数据集CDnet2014和I2R上的实验结果表明：DFDCNN不仅在动态背景、光照变化和阴影存在的复杂场景中具有更好的检测性能,而且在小目标存在的场景中也具有较好的检测效果.     

基于视觉显著性的SAR遥感图像NanoDet舰船检测方法

在合成孔径雷达遥感图像中,舰船由金属材质构成,后向散射强;海面平滑,后向散射弱,因此舰船是海面背景下的视觉显著目标。然而,SAR遥感影像幅宽大、海面背景复杂,且不同舰船目标特征差异大,导致舰船快速准确检测困难。为此,该文提出一种基于视觉显著性的SAR遥感图像NanoDet舰船检测方法。该方法首先通过自动聚类算法划分图像样本为不同场景类别;其次,针对不同场景下的图像进行差异化的显著性检测;最后,使用优化后的轻量化网络模型NanoDet对加入显著性图的训练样本进行特征学习,使系统模型能够实现快速和高精确度的舰船检测效果。该方法对SAR图像应用实时性具有一定的帮助,且其轻量化模型利于未来实现硬件移植。该文利用公开数据集SSDD和AIR-SARship-2.0进行实验验证,体现了该算法的有效性。      

基于金字塔池化网络的道路场景深度估计方法

针对从单目视觉图像中估计深度信息时存在的预测精度不够准确的问题,该文提出一种基于金字塔池化网络的道路场景深度估计方法。该方法利用4个残差网络块的组合提取道路场景图像特征,然后通过上采样将特征图逐渐恢复到原始图像尺寸,多个残差网络块的加入增加网络模型的深度;考虑到上采样过程中不同尺度信息的多样性,将提取特征过程中各种尺寸的特征图与上采样过程中相同尺寸的特征图进行融合,从而提高深度估计的精确度。此外,对4个残差网络块提取的高级特征采用金字塔池化网络块进行场景解析,最后将金字塔池化网络块输出的特征图恢复到原始图像尺寸并与上采样模块的输出一同输入预测层。通过在KITTI数据集上进行实验,结果表明该文所提的基于金字塔池化网络的道路场景深度估计方法优于现有的估计方法。     

Context prior-based with residual learning for face detection: A deep convolutional encoder–decoder network

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder–decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder–decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://github.com/zzxcoder/EvaluationFHEDN.     

BURSTS: A bottom-up approach for robust spotting of texts in scenes

In this paper, we present a bottom-up approach for robust spotting of texts in scenes. In the proposed technique, character candidates are first detected using our proposed character detector, which leverages on the strengths of an Extremal Region (ER) detector and an Aggregate Channel Feature (ACF) detector for high character detection recall. The real characters are then identified by using a novel convolutional neural network (CNN) filter for high character detection precision. A hierarchical clustering algorithm is designed which combines multiple visual and geometrical features to group characters into word proposal regions for word recognition. The proposed technique has been evaluated on several scene text spotting datasets and experiments show superior spotting performance.     

Tricolor Attenuation Model for Shadow Detection

Shadows, the common phenomena in most outdoor scenes, bring many problems in image processing and computer vision. In this paper, we present a novel method focusing on extracting shadows from a single outdoor image. The proposed tricolor attenuation model (TAM) that describe the attenuation relationship between shadow and its nonshadow background is derived based on image formation theory. The parameters of the TAM are fixed by using the spectral power distribution (SPD) of daylight and skylight, which are estimated according to Planck's blackbody irradiance law. Based on the TAM, a multistep shadow detection algorithm is proposed to extract shadows. Compared with previous methods, the algorithm can be applied to process single images gotten in real complex scenes without prior knowledge. The experimental results validate the performance of the model.     

Ridge Network Detection in Crumpled Paper via Graph Density Maximization

Crumpled sheets of paper tend to exhibit a specific and complex structure, which is described by physicists as ridge networks. Existing literature shows that the automation of ridge network detection in crumpled paper is very challenging because of its complex structure and measuring distortion. In this paper, we propose to model the ridge network as a weighted graph and formulate the ridge network detection as an optimization problem in terms of the graph density. First, we detect a set of graph nodes and then determine the edge weight between each pair of nodes to construct a complete graph. Next, we define a graph density criterion and formulate the detection problem to determine a subgraph with maximal graph density. Further, we also propose to refine the graph density by including a pairwise connectivity into the criterion to improve the connectivity of the detected ridge network. Our experimental results show that, with the density criterion, our proposed method effectively automates the ridge network detection.     

面向双模态红外图像融合算法选取的联合可能性落影构造

针对现实场景中双模态红外图像融合对异类差异特征协同优化融合的需求,且现有差异特征属性无法根据差异特征多个属性的变化针对性地调整融合算法进行有效驱动,导致融合效果差的问题,提出了面向双模态红外图像融合算法选取的联合可能性落影构造方法。首先计算双模态红外图像多融合算法下不同差异特征的融合有效度、统计差异特征分布特性;再构造差异特征融合有效度的可能性分布,通过最小二乘估计法拟合可能性分布函数;然后通过择优比较法对不同差异特征融合有效度的可能性分布进行对比分析,确定差异特征可能性分布函数投影权重,构造联合可能性落影函数;最后分析联合可能性落影函数截集水平,结合差异特征分布特性构建融合性能指标动态选取最优融合算法。实验结果表明,本文方法所选出的最优融合算法在主客观综合分析上优于其他算法,验证了本文将联合可能性落影运用于双模态红外图像最优融合算法选取中有效性和合理性。