基于改进胶囊网络的绝缘子破损识别与定位

针对接触网绝缘子破损识别,传统的特征匹配和神经网络分类识别率较低,同时因其需要人工提取和训练等问题,识别速率也较慢.相比传统卷积神经网络(CNN),胶囊网络(CapsNet)首次采用矢量作为输入,可以很好的保留目标的方向,角度等特征信息,更适合于识别复杂背景下的绝缘子.因此提出一种基于改进胶囊网络和CV模型结合的绝缘子破损识别算法,通过1×1归约层和3×3卷积层简化传统9×9胶囊网络的卷积层,并采用优化算法进行参数寻优,缩短训练权重时间,同时输出量保留方向角度,能更准确对棒形、针式和蝶式绝缘子破损情况进行分类.最后与AlexNet、YOLO、局部特征分析等方法进行了对比.通过对绝缘子巡检图像应用本文方法可得,绝缘子识别率提高到95％,实时速率达到32帧/s,所提出的绝缘子破损识别方法可以准确、迅速的从复杂背景识别出绝缘子,并准确的找到绝缘子破损的位置,大大提高了输电线路智能巡检的效率.     

高海拔地区公路病害精准识别技术分析

为了建立高海拔地区公路病害自动化精准识别技术，文中在青藏公路选取研究路段，通过无人机航拍方式获取公路数字化影像，分别利用支持向量机和最小距离算法进行图像分类，目标是识别病害类型、计算病害面积占比和路面破损率。经过检测，两种算法的病害识别准确率分别为96.97%、92.04%，支持向量机的分类识别效果更好。测试路段的主要病害类型为修补、坑槽、沉陷，根据面积占比和换算系数计算出路面破损率为1.637%。     

一种BP神经网络纸币图像识别方法的研究

介绍了BP神经网络的结构特点与原理,基于此种方法,对几种纸币的面额与方向进行了识别,其中权值的修改算法采用LM算法,并对算法的基本思路作了阐述,识别仿真结果表明,BP神经网络具有较高的准确性。     

MIV和GA-SVM在葡萄酒类识别的应用研究

因葡萄酒中含有许多化学成分因子,而这些因子之间有非线性和冗余的特点,所以如何从这些因子中选取主要的特征因子来代替诸多因子来识别出葡萄酒的种类是有必要的。为此,提出了一种基于MIV和GA-SVM的模型来解决以上问题。首先,采用4组的交叉验证法(CV)和遗传算法(GA)对训练集样本进行分类测试,从而找出支持向量机(SVM)神经网络的最佳参数;接着采取平均影响值算法(MIV)计算出13种葡萄酒的化学成分特征因子的平均影响值,根据影响值的排序采取不同的因子组合,并根据训练好的SVM神经网络对测试集进行网络仿真。结果表明,选取MIV排序值的前6或7个作为葡萄酒的主要特征因子,以此减少了神经元的输入变量,而分类准确率非常高,完全可以达到分类效果。证明了该模型的有效性。     

结合迁移学习和Inception-v3模型的路面干湿状态识别方法

针对传统方法对路面干湿状态识别分类正确率较低的情况,提出了基于迁移学习的路面干湿状态识别方法。利用深度卷积神经网络强大的特征学习和表达能力,自动学习干湿路面的特征,并采用迁移学习的方法将Inception-v3模型在ImageNet图像数据集上学习得到的知识深度迁移至路面干湿状态识别任务。实验结果表明,所提算法在测试集上测得的分类准确率约为94.5%,与非迁移学习算法和基于底层视觉特征识别学习的算法相比,具有更高的准确性和良好的鲁棒性,以及较强的泛化能力。     

改进粒子群优化BP神经网络的轮毂分类算法

针对轮毂识别系统前期图像特征提取误差较大时分类准确性降低的问题,提出了基于改进粒子群算法优化BP神经网络的轮毂识别模型。在标准粒子群中引入遗传算法的变异因子、惯性权重、时间因子、速度边界限制和反弹策略,以改进粒子群算法,从而提高寻找最优阈值与权值的性能。经过与不同算法的对比数据看出,采用改进粒子群优化BP神经网络算法的分类识别率比其他算法提高了9%左右,且收敛速度、收敛精度均有提高,证明了所提IPSO(improved particle swarm optimization)算法的有效性。     

破损水泥混凝土路面修补技术分析

本文分析总结了旧水泥混凝土路面常见的破损分类和维修方法,从路面破损状况等级和施工方法方面,重点介绍了四种破损修补方案：条带罩面法修补、扩缝粘结法、全厚修补法、快速注浆法。     

基于MEEMD和SVM的贯通式同相牵引直接供电系统牵引网故障识别

针对贯通式同向牵引直接供电系统牵引网故障识别,提出了一种基于改进总体平均经验模态分解和支持向量机的故障识别方法.利用PSCAD仿真平台搭建3种不同类型(雷击故障,雷击干扰,接地故障)的故障模型,通过模拟仿真得到故障暂态电流,并对其解耦得到故障电流相模分量.采用MEEMD算法对故障电流相模分量进行分解,提取特征向量,通过SVM工具箱进行故障识别.为了验证SVM在故障识别中的优越性,通过对比概率神经网络故障识别,结果表明SVM在故障识别中具有更高的正确率.     

多通道卷积神经网络图像识别方法

为了更好地利用图像数据中隐含的特征信息,将多方向梯度信息作为边缘信息的基本表达,提出了一种基于图像梯度的多通道卷积神经网络图像识别方法。先将图像进行Sobel算子处理,得到水平方向、垂直方向及两个对角方向的4个梯度图像。然后,建立4个多层卷积神经网络,学习4个不同方向梯度图像的特征。再将4个不同方向的特征进行随机化特征融合,得到样本的特征后经过批标准化处理。最后,通过分类器得到分类结果。在数据库Cifar-10和MNIST上进行了验证,验证结果表明:本文提出的模型具有较好的泛化能力,相比单通道卷积神经网络,在两个数据库中识别错误率分别降低了9.85%和0.38%。     

基于支持向量机与BP神经网络的裂缝识别算法研究

为了研究训练样本数量对裂缝识别算法效果的影响,在少样本和多样本的训练条件下建立了基于支持向量机和BP神经网络两种裂缝识别算法。两种算法的对比测试结果表明:在少样本情况下基于支持向量机的算法识别效果明显优越于基于BP神经网络的算法,适合工程使用初期、裂缝样本较少的情况;在多样本情况下两种算法的识别效果基本一致,均可以用于工程上。     

Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification

Abstract: This article presents a Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images. The proposed method uses a pavement distress image enhancement algorithm to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation. To extract linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a Beamlet transform‐based algorithm is applied. The crack segments are then linked together and classified into four types: vertical, horizontal, transversal, and block. Simulation results show that the method is effective and robust in the extraction of cracks on a variety of pavement images.     

A grid-based classification and box-based detection fusion model for asphalt pavement crack

Crack identification is essential for the preventive maintenance of asphalt pavement. Through periodic inspection, the characteristics of existing and emerging cracks can be obtained, and the pavement condition index can be calculated to assess pavement health. The most common method to estimate the area of cracks in an image is to count the number of grid cells or boxes that cover the cracks in an image. Accurate and efficient crack identification is the premise of crack assessment. However, the current patch-based classification method is limited by the receptive field and cannot be used to directly classify cracks. Furthermore, the postprocessing algorithm in anchor-based detection is not explicitly optimized for crack topology. In this paper, a new model, which is the fusion of grid-based classification and box-based detection based on You Only Look Once version 5 (YOLO v5) is developed and described for the first time. The accuracy and efficiency of the model are high partly due to the implementation of a shared backbone network, multi-task learning, and joint training. The non-maximum suppression (NMS)–area-reduction suppression (ARS) algorithm is presented to filter redundant, overlapping prediction boxes in the postprocessing stage for the crack topology, and the mapping and matching algorithm is proposed to combine the advantages of both the grid-based and box-based models. A double-labeled dataset containing tens of thousands of asphalt pavement images is assembled, and the proposed method is verified on the test set. The fusion model has superior performance over the individual classification and detection models, and the proposed NMS-ARS algorithm further improves the detection accuracy. Experimental results demonstrate that the presented method effectively realizes automatic crack identification for asphalt pavement.     

Neural Network for Rapid Depth Evaluation of Shallow Cracks in Asphalt Pavements

Abstract:   Rapid and nondestructive evaluation of pavement crack depths is a major challenge in pavement maintenance and rehabilitation. This article presents a computer-based methodology with which one can estimate the actual depths of shallow, surface-initiated fatigue cracks in asphalt pavements based on rapid measurement of their surface characteristics. It is shown that the complex overall relationship among crack depths, surface geometrical properties of cracks, pavement properties, and traffic characteristics can be learnt effectively by a neural network (NN). The learning task is facilitated by a database that includes relevant traffic and pavement characteristics of Florida's state highway network. In addition, the specific data used for the NN model development also contained laser-scanned microscopic surface geometrical properties of cracks in 95 pavement sections and pavement core samples scattered within five counties of Florida. Relatively advanced training algorithms were investigated in addition to the Standard Backpropagation algorithm to determine the optimal NN architecture. In terms of optimizing the NN training process, the "early stopping method" was found to be effective. The crack depth evaluation model was validated based on an unused portion of the database and fresh core samples. The results indicate the promise of NN usage in nondestructive estimation of shallow crack depths based on crack-surface geometry and pavement and traffic characteristics .     

Quantitative road crack evaluation by a U-Net architecture using smartphone images and Lidar data

Road cracks are a major concern for administrators. Visual inspection is labor-intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U-Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns.     

Pavement Distress Analysis Using Image Processing Techniques

Abstract: We demonstrate the feasibility of applying image processing techniques to the analysis of pavement distress due to cracking. Pavement image samples were obtained using a custom-designed data acquisition system called the Automatic Crack Monitor (ACM). The image samples conteining pavement cracks are parameters, are extracted using techniquesw described in this paper. The crack parameters are necessary measures used in calculations of the Pavement Serviceability Index (PSI), which is used by highway maintenance engineers to decide whether a certain pavement section needs to be repaired. Experimental results are shown and the potential harware implementation of the developed techniques is also discussed.     

A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory

Pavement cracking is one of the main distresses presented in the road surface. Objective and accurate detection or evaluation for these cracks is an important task in the pavement maintenance and management. In this work, a new pavement crack detection method is proposed by combining two‐dimensional (2D) gray‐scale images and three‐dimensional (3D) laser scanning data based on Dempster‐Shafer (D‐S) theory. In this proposed method, 2D gray‐scale image and 3D laser scanning data are modeled as a mass function in evidence theory, and 2D and 3D detection results for pavement cracks are fused at decision‐making level. The experimental results show that the proposed method takes advantage of the respective merits of 2D images and 3D laser scanning data and therefore improves the pavement crack detection accuracy and reduces recognition error rate compared to 2D image intensity‐based methods.     

基于计算机视觉技术的结构表面裂缝检测方法研究

计算机视觉技术用于混凝土结构表面裂缝检测,具有现场检测方便、效率高、客观性强的特点,但图像数据分析是该技术的核心,其中裂缝提取与定量测量较为复杂。为提高裂缝图像处理效率和准确率,将深度学习和数字图像处理技术相结合,提出一种裂缝检测方法。建立基于深度卷积神经网络的裂缝识别模型,在图像上自动定位裂缝并结合图像局域阈值分割方法提取裂缝。在裂缝宽度定量测量方面,采用双边滤波算法和三段线性变换对裂缝图像进行预处理,提高了裂缝边缘识别的精确度。通过改进边缘梯度法,实现裂缝最大宽度的定位和裂缝最大宽度的自动获取。该研究为全自动识别裂缝图像及高精度测量裂缝宽度提供了一种解决方法。     

基于图像分析的节理岩体单轴压缩损伤演化研究

 为定量地研究节理岩体的损伤演化规律,对岩体石膏模型试件单轴压缩试验过程中拍摄的表面数字图像进行处理分析。编制Matlab程序,实现单个裂纹的识别、裂纹长度和方位角、总裂纹面积分数和总裂纹分形维数的计算。对节理倾角和节理连通率这2个参数组合变化下的试件表面裂纹图像的分析结果为：(1) 试件表面总裂纹面积分数和总裂纹分形维数变化规律基本相似,在各节理连通率和各节理倾角下,2个参量都随轴向应变的增加而增大;(2) 可将试件分为两大组,节理倾角为0°,15°,75°,90°试件(劈裂破坏为主)和节理倾角为30°,45°,60°的试件(剪切破坏为主),第1组试件表面的总裂纹面积分数和总裂纹分形维数值都高于第2组试件;(3) 具有相同节理连通率的试件,在试验开始点不同节理倾角的总裂纹面积分数基本相同,在峰值荷载点和试验结束点的总裂纹面积分数随节理倾角的变化曲线基本呈V型(最小值在节理倾角为45°处,最大值在节理倾角为0°处);(4) 表面裂纹在试验开始时和加载过程中的各向异性分布特征,可以用裂纹面积分数沿裂纹方位角的分布图来表征。研究结果表明,表面裂纹图像分析可以有效地定量研究节理岩体试件的各向异性损伤演化特征。     

面层与基层层间摩擦系数对应力强度因子影响的研究

开裂为沥青混凝土路面中存在的主要问题。基于线弹性断裂力学理论,采用ABAQUS软件中的平面应变单元法,分别分析了变温作用下裂缝的长度、面层与基层层间摩擦系数态对含表面裂缝和含反射沥青混凝土路面的应力强度因子分布规律的影响。得出了含表面裂缝和反射裂缝路面体的应力强度因子均随着裂缝长度的增加逐渐增大。表面裂缝裂尖位于面层中时,应力强度因子随着摩擦系数的增加而逐渐减小;裂尖位于基层中时,应力强度因子随着摩擦系数的增加而逐渐增大。反射裂缝裂尖位于基层中时,应力强度因子随着摩擦系数的增加而减小;反射裂缝裂尖位于面层中且长度小于某一值时,应力强度因子随着摩擦系数的增加而增大;反射裂缝裂尖位于面层中且裂缝长度大于某一值时,应力强度因子随着摩擦系数的增加而减小。     

半刚性基层沥青路面裂缝成因分析及控制措施

随着我国公路建设的快速发展,道路病害尤其是沥青路面裂缝病害日益增多,裂缝对道路的危害作用极大,本文分析了半刚性基层沥青路面裂缝的类型及裂缝原因,并提出了预防路面开裂的措施。