When performing robot/vehicle localization using ground penetrating radar (GPR) to handle adverse weather and environmental conditions, existing techniques often struggle to accurately estimate distances when processing B-scan images with minor distinctions. This study introduces a new neural network-based odometry method that leverages the similarity and difference features of GPR B-scan images for precise estimation of the Euclidean distances traveled between the B-scan images. The new custom neural network extracts multi-scale features from B-scan images taken at consecutive moments and then determines the Euclidean distance traveled by analyzing the similarities and differences between these features. To evaluate our method, an ablation study and comparison experiments have been conducted using the publicly available CMU-GPR dataset. The experimental results show that our method consistently outperforms state-of-the-art counterparts in all tests. Specifically, our method achieves a root mean square error (RMSE), and achieves an overall weighted RMSE of 0.449 m across all data sets, which is a 10.2% reduction in RMSE when compared to the best state-of-the-art method.

Optimizing the generalization performance of road surface crack models in practical applications represents a challenging task. Especially for thin and irregular cracks with random and expansive topologies, the loss functions used in current deep learning-based crack segmentation models are sensitive to single pixels, which tends to cause the model to overfit the training data, diminishing its generalization ability in real-world scenarios. Therefore, we take the loss function as a starting point and explore the introduction of proactive fault tolerance mechanisms into the training process of the crack segmentation model, which is called Proactive Fault Tolerance Loss (PFT Loss), to enhance the generalization capability of model in actual applications. Specifically, the PFT Loss function establishes correlations between the segmentation prediction pixels and the corresponding labeled pixels within the neighborhood window using Markov Random Fields (MRFs). The correlation is used as a reference for predicting relative shifts in segmented pixels. Proactive Fault Tolerance is performed on the loss between labeling and prediction to achieve a more natural and adaptive training method for crack segmentation. Full experiments are conducted on five public crack datasets and one self-constructed dataset. The experimental results indicate that the model trained with PFT Loss has better segmentation performance compared to other loss functions.

The accurate perception of subsurface objects and defects is vital in airport routine maintenance. For the complex subsurface environment of the airport runway, to obtain the high-performance segmentation of typical subsurface targets, a multistream attention segmentation network is proposed. The network takes the ground penetrating radar (GPR) raw data and the preprocessed B-scan image as multistream input. It carries out sufficient feature fusion on multiple modals, scales, and levels to get robust feature representation. Furthermore, we proposed two attention mechanisms suitable for multistream feature fusion, which can learn more effective features. We validate our method on an actual airport runway dataset. Experimental results show that our method can obtain an F1-measure of 82.08%, 89.12%, and 82.54% for three typical subsurface targets: void, pipe, and steel mesh, respectively.

Ground-penetrating radar (GPR) is widely used for detecting airport runway subsurface defects. The trailing interference in the GPR data disguises subsurface defect responses, which seriously affects the accuracy of subsurface defect detection. To tackle the challenge of subsurface defects detection under trailing interference in real scenarios, a new multimodal fusion network referred to as $\mathrm {M^{2}}$ FNet is proposed. Based on the premise that the trailing signal is highly similar across all adjacent A-scans, the model employs a transformer encoder to extract global features of the signal with long-distance correlation. In contrast, the subsurface defects only show echo characteristics in a few adjacent B-scans. The phase of the trailing and the target signal is opposite, which is easy to discover from the Top-scan view. Thus, a hybrid convolutional neural network structure is used to extract local features from GPR images of different views. This dual network structure extremely enhances the representation learning of GPR data. In order to investigate various subsurface defects and trailing interference collected by multiple GPR systems under different conditions, the first large-scale hybrid dataset called ASD-GPR is created. Transfer learning is employed to enhance the model’s ability to detect rare defects by fine-tuning it for real-world situations, differing from synthetic training data scenarios. The results of the experiments reveal that $\mathrm {M^{2}}$ FNet outperforms state-of-the-art object detection methods in various real-world scenarios, demonstrating superior performance in detecting subsurface defects.

及时准确地检测机场跑道地下病害对保障飞行安全至关重要,由于机场道面结构层复杂,其电磁波传播环境复杂、噪声强度大,致使地下病害的探地雷达数据特征被严重干扰.为此,提出了一种基于尺度融合的机场跑道地下病害检测算法SF-SSD.首先,在VGG16主干网络上设计具有更宽感受野的RFB模块,抑制了病害周围的噪声干扰,提取更多原始雷达数据特征;然后,使用尺度融合的方式融合网络浅层特征,获取不同类型病害间的细微差异,形成高辨识度的病害细节纹理特征;最后,根据6种不同分辨率的特征图生成6种尺度先验框进行类别预测和位置回归,通过非极大值抑制的方式筛除冗余的先验框.在真实机场跑道地下病害数据集上进行了测试,并与目前5种经典目标检测算法进行对比.实验结果表明,SF-SSD算法可以较好地从辨识度低、噪声强度大的雷达数据中完成病害自动检测,并取得了最高的平均准确率,达到了82.18％.

In the task of detecting targets under airport pavement, B-scan maps generated by Ground Penetrating Radar （GPR） , have complex backgrounds and lots of noise, especially a single B-scan map cannot reflect the complete information of an underground target. To solve these problems, a Three-Dimensional Channel and Spatial Attention UNet （3D-CSAUNet） , model was established to automatically detect the underground targets. Firstly, a Three-Dimensional Channel and Spatial parallel attention Block （3D-CS-Block） , was designed to make the model focus on the underground target information in radar C-scan and suppress the interference of backgrounds and noise. Secondly, in order to enhance the capability of 3DCS-Block in feature extraction, a multi-scale 3D segmentation model was designed to extract feature maps of different sizes from the radar C-scan. Finally, the cross-entropy loss function was employed to calculate the loss value of feature map under each scale to improve the detection accuracy of the model. On a real dataset of targets under airport pavement, compared with 3D-Fully Convolutional Network （3D-FCN） , , 3D-UNet and other algorithms, 3D-CSA-UNet has the average F1 score in terms of the pixel level segmentation for void, rebar and parallel rebar targets increased by at last 12. 33, 9. 05 and 11. 05percentage points. Experimental results show that 3D-CSA-UNet can meet the real engineering requirements well.

针对已有方法对机场道面地下管线定位误差较大的问题,提出一种机场道面地下管线三维定位算法。首先,对探地雷达所成管线的B-scan图像进行预处理,将处理后的图像输入Faster-RCNN网络中,对B-scan图像中的管线进行目标识别;其次,由于管线目标符合双曲线形态特征,采用双曲线顶点获取算法确定顶点位置;最后,设计三维空间直线拟合（three-dimensional space line fitting,TDSLF）算法来判断地下管线的具体位置,进行地下管线的三维重构。所提算法实现了地下管线的自动识别与定位,与真实机场道面地下管线实际位置的最大误差仅为4 cm。

在使用探地雷达(GPR)生成的Bscan图像进行地下目标检测时,当前基于深度学习的目标检测网络模型存在训练样本需求量高、耗时长,不能区分目标显著程度,难以识别复杂目标等问题.针对以上问题,提出一种基于直方图的双阈值分割算法.首先,根据地下目标的GPR图像直方图分布特性,快速从直方图中计算出分割地下目标所需的两个阈值;然后,采用支持向量机(SVM)和LeNet的组合分类器模型对分割结果进行分类识别;最后,进行分类结果整合并统计精确度数值.相较于传统的最大类间方差法(Ostu)、迭代法等阈值分割算法,所提算法获得的地下目标分割结果结构更加完整,并且几乎不含噪声.在真实数据集上,所提算法的平均识别准确率达到了90％以上,比仅使用单一分类器的平均识别准确率提高40％以上.实验结果表明,所提算法能够同时有效分割显著和非显著性地下目标,且采用的组合分类器能够获得更好的分类结果,适用于小样本数据集的地下目标自动检测和识别.

The detection and restoration of subsurface defects are essential for ensuring the structural reliability of airport runways. Subsurface inspections can be performed with the aid of a robot equipped with a Ground Penetrating Radar (GPR). However, interpreting GPR data is extremely difficult, as GPR data usually contains severe clutter interference. In addition, many different types of subsurface defects present similar features in B-scan images, making them difficult to distinguish. Consequently, this makes later maintenance work harder as different subsurface defects require different restoration measures. Thus, to automate the inspection process and improve defect identification accuracy, a novel deep learning algorithm, MV-GPRNet, is proposed. Instead of traditionally using GPR B-scan images only, MV-GPRNet utilizes multi-view GPR data to robustly detect regions with defects despite significant interference. It originally fuses the 3D feature map in C-scan data and the 2D feature map in Top-scan data for defect classification and localization. With our runway inspection robot, a large number of real runway data sets from three international airports have been used to extensively test our method. Experimental results indicate that the proposed MV-GPRNet outperforms state-of-the-art (SOTA) approaches. In particular, MV-GPRNet achieves F1 measurements for voids, cracks, subsidences, and pipes at 91%, 69%, 90%, and 100%, respectively.

近年来,满足前后向安全的动态对称可搜索加密(dynamic symmetric searchable encryption,DSSE)一直备受关注,它可以抵抗文件注入攻击,同时限制服务器学习已删除文档的相关信息.不过大多数满足前后向安全的DSSE方案仅支持单关键词搜索,Patranabis等人在NDSS 2021会议上提出了一种支持联合搜索且满足前后向安全的动态可搜索加密方案,但该方案在某些情况下不能得到准确的查询结果,同时不能支持多用户查询.针对以上问题,通过改进不经意交叉索引(oblivious cross tags,OXT)协议,提出了一种支持联合搜索的多用户动态对称可搜索加密方案.该方案利用有限域中元素具有乘法逆元的性质,引入了一次性盲因子,并结合数字信封技术实现了多客户端查询的功能.方案分析与实验表明,所提方案满足了前向安全与后向安全,不仅可以提供准确的联合查询功能,而且支持多客户端查询,同时计算效率仅与更新次数最低的关键词更新次数有关.

为填补室内定位方面研究的空缺,针对超声波、射频识别、红外线等定位技术定位精度不足、解决实时性较差问题,提出一种新的基于WiFi指纹滤波法的室内行人定位方法,并利用惯性导航技术进行定位结果优化.研究具体过程如下:研究分为三个阶段,第一阶段基于WiFi指纹进行室内定位估算,内容包括指纹数据库建立、相似度匹配,实现目标位置估算;第二阶段基于惯性导航的定位.内容包括步态检测、步长计算以及航向获取,完成室内行为估算;第三阶段将WiFi指纹定位与惯性导航结果相融合,进行联合定位,得到目标的最终位置坐标.结果 表明:所提方法在2.5m范围内的分布范概率和实时性均得到提高,应用效果更好.

Detection of subsurface defects is important for maintaining runway structural health and reliability. A potential solution is to employ a robot equipped with a Ground Penetrating Radar (GPR) to perform subsurface scanning. To automate the inspection process, we develop a subsurface defect detection algorithm which is a deep learning algorithm that fuses 2D planar features in each panel in GPR B-scans and 3D voxel-wise features in GPR C-scan to robustly detect regions with defects even in the presence of significant noises. Named as GPR-RCNN, we have tested our algorithm with real airport runway data collected from three international airports using our runway inspection robot. The experimental results show that our proposed GPR-RCNN achieves superior results when comparing to state-of-the-art techniques. Specifically, our method achieves F1-measures at 62%, 33%, 81%, and 87% for void, crack, subsidence and pipe, respectively.

The high-precision infrared small target detection under low-altitude background has a high application value. The existing small IR target detection methods usually fail or cause a high probability of false alarm in the highly moving and complex low-altitude backgrounds. A detection method based on a fully convolutional network in spatial and graph matching in temporal is proposed. First, a deep fully convolution regression network for highprecision small target detection is designed to obtain accurate target probability heat maps. A weighted bipartite graph matching model is established for the target trajectory association based on the targets' temporal characteristics. It uses the motion and radiation similarity between the detection results in adjacent frames to eliminate false alarms caused by random noise and clutter. Finally, it further integrates the target optical flow information into the trajectory to distinguish between real targets and fixed ground objects. Many experimental results show that the method in this paper can accurately detect small targets in complex moving backgrounds and achieve a high detection rate in the case of a low false alarm rate.

An invalid cloud region masking method based on local edge context is proposed for the compression of remote sensing images. Through analyzing the characteristics of various compression algorithms and taking the local edge information of the cloud region into account, the decompression quality is improved. First, according to the cloud mask information, labeling the connected cloud region, second, performing region growth on the labeled mask image, then differing the two mask images to obtain the local edge context, and finally different invalid cloud regions are filled with the average of the respective local edge context pixels. Using the image testing set generated from QuickBird and OrbView images, our method’s impact on six common remote sensing image compression algorithms is analyzed experimentally. The experimental results show that our masking method can improve the decompressed image quality when the compression ratio is certain. When the image quality is fixed, it can further reduce the compressed bitrate. For onboard application, our masking method can increase the onboard imaging time of the satellites, and eventually improve the onboard specifications of remote sensing satellites.

为解决新一代民航旅客服务信息系统中数据缺失不能准确进行旅客流失预测的问题,考虑旅客流失预测任务与旅客缺失数据填补任务具有相关性,提出基于部分距离的SMOTE算法和基于降噪自编码器的多任务深度学习网络模型.基于部分距离的SMOTE算法在数据不完备场景下,对少数类别已流失旅客和准流失旅客进行过采样;设计的多任务深度学习网络模型利用逐层无监督训练和有监督微调,同时进行对缺失数据填补与民航旅客流失预测.实验结果表明,相比传统处理方法,该方法能有效提高数据质量和旅客流失预测的准确性.

为便于航空公司和机场对旅客的监管和服务决策,需要对旅客潜在不文明水平进行有效分级预测.通过社会不文明行为与民航不文明行为的相似性分析,预测旅客在民航潜在的不文明行为等级,针对两类行为描述之间存在的非同源问题,通过改进SIF(smooth inverse frequency)算法设计实现基于行为特征和处罚特征联合相似度匹配的民航潜在不文明等级预测(IPBS-SIF)算法.该算法结合不文明旅客分布规律为处罚规则制定统一量化标准,实现不文明旅客的多粒度融合度量.实验结果表明,IPBS-SIF算法在预测准确度方面相比其它算法提高了12.2％-15.1％,为民航不文明旅客的行为分析和预测提供了有效的解决方案.

为解决机器阅读理解模型中存在语义向量表示不准确、信息冗余、长距离依赖等问题,提出一种基于协同注意力和自适应调整的阅读理解模型.基于余弦相似度计算问题与文档的相似度权重,根据相似度权重自适应调整文档的词嵌入,解决信息冗余问题;引入协同注意力机制,捕获文档和问题的交互信息,生成感知向量;利用自注意力机制学习文本内部的依赖关系,增强问题和文档的语义向量表示,解决长距离依赖问题,提升模型性能.实验结果表明,该模型在精确匹配和模糊匹配指标上均得到提升.

Aiming at the ambiguity and uncertainty of civil aviation master data and the lack of objectivity in weight determination,a civil aviation master data identification method based on cloud model and rough set was proposed.According to the characteristics of civil aviation master data,the most representative 7 identification indicators were selected and divided into 5 levels.The forward cloud generator was used to generate a comprehensive cloud model with each identification index belonging to each main data level.The membership degree of each entity belonging to each main data level was calculated.The rough set theory was introduced to calculate the weight of each identification index and the degree of certainty belonging to the determination degree of each main data,and the maximum certain degree was used as the master data level entity.Experimental results show that the results obtained using this method are consistent with the expected results,which verifies the effectiveness of the method and provides an idea for the main data identification.

广播式自动相关监视(ADS-B)是下一代空中交通运输系统的重要组成部分,是新航行系统中非常重要的通信和监视技术,但其协议没有提供相关的信息认证和数据加密,因此极其容易受到欺骗干扰的影响.针对ADS-B报文数据特点,采用深度学习的seq2seq模型对ADS-B报文数据进行重构,通过重构误差来检测异常,并对数据进行特征扩展,使模型能更好的捕捉数据的时间依赖性.实验结果表明,所采用的方法优于传统的机器学习方法,且在数据特征扩展后,模型检测效果提升.相比于现有的欺骗干扰检测方法,该方法不需要改变ADS-B系统的协议,也不需要额外的节点或传感器参与,具有一定的适应性和灵活性.