一种求解TSP问题的离散蝙蝠算法

蝙蝠算法是一种新型的群智能优化算法,在求解连续域优化问题上取得了较好的优化效果,但在离散优化领域的应用较少。研究了求解TSP问题的离散蝙蝠算法,设计了相关操作算子实现算法的离散化,并引入逆序操作使算法跳出局部最优。对TSPLIB标准库中若干经典实例进行测试并与粒子群和遗传算法进行对比分析,结果表明设计的离散蝙蝠算法无论在求解质量还是求解效率上都有明显优势,是一种高效的优化算法。     

基于改进型模糊聚类算法的植物病斑检测

针对植物病害图像成分复杂 、病斑排列无规则等特点,提出了一种改进型模糊聚类的病斑检测算法。该算法采用Markov随机场与模糊聚类算法耦合策略,能够有效解决植物病斑检测时的模糊性和随机性问题。仿真实验表明,改进后的算法能够实现植物病斑的自适应检测,鲁棒性较好。然而,对于Markov与模糊聚类算法的最佳耦合方式及对于如何减少算法的运算量仍需作深入的研究。     

旋转对称三角传感器激光散斑的仿真研究

散斑是激光三角传感器测量不确定度极限的根本影响因素。提出了一种用于旋转对称激光三角传感器的激光散斑的仿真方法,获得了仿真散斑图像。在旋转对称的三角传感器中,投射的激光点在检测器上被成像为一个环,从而散斑也相应是圆弧形。研究了散斑的特性,该散斑在环的半径方向上服从主观散斑的特性,其尺寸由光学系统的数值孔径决定。而在环的切线方向上,其本质上是客观散斑,由于光学系统存在折返光路,其尺寸由物体到检测器的光程、投射的激光光斑尺寸和成像圆环的半径决定。实验结果表明,仿真结果与散斑理论一致。基于仿真给出了对旋转对称三角传感器位移测量不确定度极限的分析,结果表明,使用旋转对称形式的传感器光学布局,在相同的光学系统数值孔径和使用同样的灰度质心算法的情况下,可达到传统激光三角测量不确定度的1/5。     

基于专家PID控制的圆环链淬火系统

针对目前圆环链淬火过程中温度控制精度不高、抗干扰能力差等问题,本文提出了一种新型的基于专家PID算法的圆环链淬火控制系统. 描述了智能专家PID算法的原理和实现方法,且将其应用到淬火控制系统中,并就常规PID和专家PID在淬火控制效果上进行了对比. 运行结果表明,基于专家PID算法的温控系统可以对圆环链淬火温度实现优良的控制,并具有响应速度快、鲁棒性强的优点.     

多尺度分形维的星载舰船显著性检测

目的 星上的舰船检测需要在资源和时间受限条件下实现快速检测,并且对目标的种类和尺寸缺少先验信息的指导,更多时候还需要实现一景图像中不同尺寸舰船的检测,因此,星上舰船检测要求检测方法具有一定的自适应性,从而实现星上多变的检测场景。方法 针对这一问题,提出了一种多尺度分形维的检测方法,可以实现一景遥感图像中不同尺寸舰船目标的检测。首先,针对差分盒算法受盒子尺寸约束的限制使分形维数的计算精度受到影响的问题提出了一种改进算法,改进算法增加了拟合直线的点对数目并引入了拟合误差剔除误差点对,提高了分形维特征计算的精确度。结果 在提高了分形维计算精度的基础上,新算法利用自然物体在不同尺度上具有的自相似性,通过多尺度分形维的计算并借鉴视觉显著性中c-s算子来排除背景对目标的干扰,突出舰船目标。实验结果表明,新算法能够有效检测出一景图像中不同尺寸的舰船,优于双参数CFAR算法的检测结果。结论 本文提出的多尺度分形维的检测算法可以实现对一景图像中不同尺寸舰船目标的检测,在保证一定检测率的同时有效降低了目标检测的虚警率。     

基于模糊专家规则的离散事件辨识及其在交通诱导中的应用

传统离散事件动态系统中的离散事件状态的转换具有不确定性,其不确定性主要来自状态转向发生时刻的不确定性,而所转向的状态一般具有确定性。本文对具有状态转向时刻和转向状态的二重不确定性的离散事件动态系统进行了讨论,用模糊专家系统来对未来状态和状态迁移时刻二重不确定性的离散事件动态系统进行评价,并以智能交通系统中车辆诱导技术为例,说明了此类模糊专家规则的应用价值。本文提出了一种模拟淬火算法,通过模拟物质加温后急剧冷却的过程来求得目标函数的局部极值,以模拟淬火算法的转向概率作为模糊专家系统中的规则选择概率。该方法有效地保证了事件转换的实时性,提高了交通疏导的效率。     

光学组件面形检测中激光光斑分割与分类算法的研究

针对光学组件面形检测中激光光斑处理问题,提出了一种激光光斑分割与分类算法;该算法对光学组件面形检测中激光光斑的特点进行了分析,利用延时相关滤波、梯度阈值分割以及基于特征的分类技术,实现了激光光斑分割与分类;实验结果表明,该算法能有效区分光学组件前后表面反射光斑,提取质心重复性高、稳定可靠;该方法已商用于光学组件面形检测系统中.     

基于激光扫描的移动机器人行走环境直线检测

提出一种移动机器人行走环境直线检测算法;对激光传感器采集的环境信息作预处理,设计分割、提取规则将离散距离信息转化为具备明显特征的直线段序列,考虑传感器误差用最小二乘法拟合直线段;计算拟合误差作为直线分割提取的阈值自动调整条件,实现阈值自动调整.基于极坐标内直线协方差矩阵计算Mahalanobis距离,实现直线合并,不丢失环境信息同时降低其直线存储量.与传统的Split-and-Merge算法相比,解决了对分割阈值参数敏感的问题.经过实验证明:直线检测算法能够有效检测出直线特征.     

一种轴类零件边缘精确定位方法

针对目前的边缘检测算法存在定位精度低、处理速度慢、抗噪性能差等缺陷,提出了一种轴类零件尺寸检测的图像边缘高精度定位方法。该方法采用改进的自适应中值滤波算法、改进的Kirsch算子和在图像边缘灰度梯度方向上进行二次函数逼近高斯曲线拟合方法,实现了图像边缘亚像素高精度定位,提高了尺寸检测精度。通过对气门尺寸的计算机视觉检测实际应用,证明提出的算法精确且稳定,满足高精度视觉检测要求。     

一种基于离散小波变换的图像数字水印算法

提出了一种以离散小波变换为基础的图像数字水印算法。详细介绍了离散小波变换和基于离散小波变换的数字水印嵌入算法及检测算法。     

机械加工零件表面纹理缺陷检测

在一些对机械加工零件表面的加工精度和表面质量要求较高的自动化工业中，对机械加工零件表面纹理缺陷进行可靠的、有效的检测和分析可以大大地提高生产加工的自动化水平。为了能够对机械加工零件表面进行可靠、有效的检测，根据机械加工零件表面的纹理特点，设计了一种新的图像频域滤波器，用于增强缺陷纹理图像和抑制背景纹理对缺陷纹理检测的干扰，再通过图像分割的方法的实现了缺陷纹理和背景纹理的分割。实验结果显示，这种方法检测速度较快，尤其适用于机械精加工零件表面纹理缺陷的准实时检测。     

基于Gaussian-yolov3的铝型材表面缺陷检测

在铝型材的实际生产过程中,由于各方面因素的影响,铝型材表面会产生碰伤,刮花,凸粉等瑕疵,这些瑕疵会严重影响铝型材的质量。目前主要采用人工检测,由于铝型材表面自身含有纹路,与瑕疵区分度不高,传统人工肉眼检查十分费力,质检的效果难以控制。为解决上述问题,以铝型材表面缺陷为研究对象,使用Gaussian-yolov3为基础目标检测网络,针对铝型材表面部分条状缺陷的特性,使用变形卷积技术增强卷积的适应性。针对小缺陷检测问题,使用密集连接技术。使用数据增强扩展数据。通过对比Faster R-CNN、SSD实验,结果表明,基于Gaussian-yolov3的检测方法准确率可以达到96%,检测速度可以满足实时性要求,具有较强的实用性。     

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.     

基于反向P-M扩散的钢轨表面缺陷视觉检测

研制了一种基于反向P-M（Perona-Malik）扩散的钢轨表面缺陷视觉检测装置,该装置可 自动获取钢轨表面图像,并实现实时检测与定位钢轨表面缺陷. 钢轨图像具有光 照变化、反射不均、特征少等特点,为了在运动过程中 从复杂的钢轨表面图像提取缺陷,首先将图像进行反向P-M扩散,然后将扩散后的图像与原图像进 行差分,从而减小了上述因素的影响,最后将差分图像进行二值化操作,根据 缺陷边缘特性和面积进行滤波,分割出缺陷图像. 实验仿真和现场测试结果表明,该方法能很好地识别块状缺陷和线状缺陷,并且检测速度、精度、识别 率和误检率都能很好地满足要求.     

Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns

Visual quality inspection systems play an important role in many industrial applications. In this respect, surface defect detection is one of the problems that have received much attention by image processing scientists. Until now, different methods have been proposed based on texture analysis. An operation that provides discriminate features for texture analysis is local binary patterns (LBP). LBP was first introduced for gray-level images that makes it useless for colorful samples. Sensitivity to noise is another limitation of LBP. In this article, a new noise-resistant and multi-resolution version of LBP is used that extracts color and texture features jointly. Then, a robust algorithm is proposed for detecting abnormalities in surfaces. It includes two steps. First, new version of LBP is applied on full defect-less surface images, and the basic feature vector is calculated. Then, by image windowing and computing the non-similarity amount between windows and basic vector, a threshold is computed. In test phase, defect parts are detected on test samples using the tuned threshold. High detection rate, low computational complexity, low noise sensitivity, and rotation invariant are some advantages of our proposed approach.     

基于纹理特征的磁片表面刀纹缺陷检测

表面缺陷检测在工业生产中对产品质量可以起到有效的监督控制作用,而目前对磁性材料表面刀纹缺陷检测的方法各自存在自身的局限性,如成本过高、检测速度太慢而不能满足工业生产中实时检测的需求等。为了能够达到实时稳定的检测磁片表面刀纹缺陷的目的,本文借助于计算机工业视觉系统,基于纹理特征,通过图像预处理,采用增强缺陷部分并抑制背景部分的方法,使得各种缺陷具有了统一性,从而能设计适合的掩模来提取出缺陷部分,实现了对磁片表面刀纹缺陷的检测。实验结果表明,采用本文提出的方法可以实时完成对磁片表面刀纹缺陷的检测并且对于多种缺陷类型都适用。     

Wavelet-based defect detection in solar wafer images with inhomogeneous texture

Solar power is an attractive alternative source of electricity. Multicrystalline solar cells dominate the market share owing to their lower manufacturing costs. The surface quality of a solar wafer determines the conversion efficiency of the solar cell. A multicrystalline solar wafer surface contains numerous crystal grains of random shapes and sizes in random positions and directions with different illumination reflections, therefore resulting in an inhomogeneous texture in the sensed image. This texture makes the defect detection task extremely difficult. This paper proposes a wavelet-based discriminant measure for defect inspection in multicrystalline solar wafer images.The traditional wavelet transform techniques for texture analysis and surface inspection rely mainly on the discriminant features extracted in individual decomposition levels. However, these techniques cannot be directly applied to solar wafers with inhomogeneous grain patterns. The defects found in a solar wafer surface generally involve scattering and blurred edges with respect to clear and sharp edges of crystal grains in the background. The proposed method uses the wavelet coefficients in individual decomposition levels as features and the difference of the coefficient values between two consecutive resolution levels as the weights to distinguish local defects from the crystal grain background, and generates a better discriminant measure for identifying various defects in the multicrystalline solar wafers. Experimental results have shown the proposed method performs effectively for detecting fingerprint, contaminant, and saw-mark defects in solar wafer surfaces.     

A smart surface inspection system using faster R-CNN in cloud-edge computing environment

Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNN-based image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster R-CNN algorithm in the cloud-edge computing environment. The faster R-CNN as a CNN-based object detection method can efficiently identify defects in complex product images and the cloud-edge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time.     

Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces

The limited receptive area (LIRA) neural classifier is proposed for texture recognition of mechanically treated metal surfaces. It may be applied in systems that have to recognize position and orientation of complex work pieces during micromechanical device assembly as well as in surface quality inspection systems. The performance of the proposed classifier was tested on a specially created image database with four texture types corresponding to metal surfaces after milling, polishing with sandpaper, turning with lathe and polishing with file. The promising recognition rate of 99.8% was obtained.     

Inspecting mixed textured lens collar images using discrete Fourier transformation

Superficial and electroplating defects are the most commonly seen flaws on the lens collar. The former arose from cutter offset or chip winding during the cutting process, while the later occurred if the surface was stained with rough or foreign material during the electroplating process. Relying on human inspection to ensure quality of a lens collar was time consuming and accounted for occupational injury. Thus, implementation of automatic inspection technology became invertible in the mass production environment. Since the texture on the surface of lens collar was not only regular but also statistical, the system used image restoration based on discrete Fourier transformation (DFT) to detect defects embedding on those two types of textures.