高压输电线路巡线机器人障碍物视觉检测识别研究

障碍物的检测识别与定位是高压输电线路自主巡线机器人中的关键技术之一.针对220 kV单分裂输电线路的结构特点,提出了一种基于视觉传感器的障碍物检测识别方法.首先,对采集的图像进行膨胀、腐蚀和高斯平滑等处理,以减少图像噪声;然后,利用Otsu算法对Canny算子进行改良来提取图像边缘,以减少光线变化带来的影响;最后,提取边缘图像中某些图形基元并施加结构约束,实现障碍物的检测识别.大量的试验研究表明,该方法能有效地检测识别高压输电线路的导线、防震锤、悬垂绝缘子和耐张绝缘子等结构物.     

应用激光雷达与相机信息融合的障碍物识别

针对探测器在地外星体表面软着陆过程中的障碍物识别问题,提出了一种融合三维点云数据与灰度图像数据进行精确障碍物识别的方法。首先利用坐标转换将灰度图像与三维点云归一化到同一坐标系下,实现传感器数据的融合。然后采用改进K均值聚类算法对预处理后灰度图像进行图像分割,生成光学障碍图。最后利用开源库PCL(Point Cloud Library)对激光雷达生成的三维激光点云数据进行处理,采用随机采样一致性算法提取着陆区地形水平面,对去除水平面后的点云数据进行点云分割,分离出突起物、凹坑等障碍物,并通过激光雷达与相机转换坐标系,投影到像平面,生成最终障碍图。     

乐谱图像倾角快速检测方法

计算机光学乐谱识别技术是将传统的纸质型乐谱转化为计算机能够“读懂”的数字音乐，在计算机音乐领域中具有重要的应用价值、乐谱识别系统的输入是乐谱扫描图像，而扫描过程中出现的图像倾斜现象，会给识别过程中的谱线定位和谱段切割带来诸多困难，必须对图像作有效的倾斜校正以保证系统的性能。为此，提出了一种快速的乐谱图像倾角检测方法。该方法首先利用乐谱文档的自身结构特点，对图像进行预处理，滤除乐谱图像中不具备方向性的干扰像素，然后通过多组图像水平投影队列间的交叉相关性计算对倾角进行检测。其特点是在确保检测倾角精度的同时具有非常高的执行效率。实验结果表明这一方法是有效、实用的。     

Robot Localization in Indoor Environments Using Radio Frequency Identification Technology and Stereo Vision

In this paper, a novel method of obstacle recognition and localization for mobile robots using radio frequency identification (RFID) technology and stereo vision is proposed. It is inexpensive, flexible and easy to use in practical environments. As information about the obstacles or environment can be written in ID tags, the proposed method can detect the obstacles easily and quickly compared with other methods. RF is not so stable, so the Bayes rule was introduced to calculate the probability where the ID tag exists in order to improve the accuracy of localizing obstacles with the ID tags. Then the stereo camera starts to process the image of the region of interest where the obstacle exists. The proposed method does not need to process all the image and easily gets some information about obstacles such as size, and color, and thus decreases the processing computation. Research on RFID technology integrating stereo vision to localize an indoor mobile robot has also been performed. This paper introduces the architecture of the proposed method and some experimental results.     

基于波前编码技术的虹膜识别系统

自动虹膜识别是一种非入侵式身份认证方法。目前的虹膜识别系统中图像采集系统的景深非常有限,获取清晰虹膜图像需要用户的密切配合,这阻碍了虹膜识别系统的广泛应用。本文提出基于波前编码系统的虹膜识别系统,极大地扩大了虹膜识别系统的景深,提高了虹膜识别系统使用的友好性。     

基于AdaBoost算法的智能轮椅避障研究

随着计算机图像处理能力和技术的发展,视觉传感器在移动机器人导航和障碍物识别中的应用越来越受到重视.将AdaBoost算法用于智能轮椅的障碍物识别,在Visual C++6.0平台下,用AdaBoost算法训练得到用于障碍物检测的强分类器,然后利用该分类器进行检测出目标障碍物,并用模糊神经网络的方法对轮椅的声纳信息,视觉...     

Canny边缘检测算法在机器人视觉中的应用

OpenCV是一个以C函数为主,提供多语言接口,轻量并高效的计算机视觉库,实现了图像处理和计算机视觉方面的多种通用算法。利用其Canny边缘检测算法,实现模糊图像的边缘检测,参照L2BOT视觉机器人实例,对单一背景模式下基于OpenCV的条形码与障碍物识别进行研究,并对机器人移动路线最优选择提供一种参考模型。     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

网格提取的图像修复方法

数字图像的修复,一般都需要人们用手工来标记待修复的区域。提出了一种新的方法,能够自动标记图像中的待修复区域——网状障碍物。首先将网状障碍物看作网格,利用网格是由空间中有规律两组线交叉形成的这一显著特点,然后通过计算来提取覆盖在图像上的网状障碍物,这样就不需要人们用手工来标记。为了保证得到的网格线能够完全覆盖图像上的网状障碍物,把提取的网格线进行适当的膨胀,实验证明该方法是有效的,且比较容易实现。     

基于单目视觉的水面目标识别与测距方法研究

无人船在自主航行过程中,如何探测障碍物种类与距离是值得重点研究的问题.现阶段的无人船主要采用激光雷达、超声波等设备探测水面障碍物,但此类方法并不能识别出障碍物的具体类型.提出采用基于单目视觉的目标检测与测距算法,不仅能够检测出无人船前方是否有障碍物,还能够对水面障碍物进行识别和距离检测.首先通过训练好的Mask R-C...     

架空输电线路巡线机器人的视觉导航

巡线机器人沿相线行走时必须探测识别和定位各种障碍物,并根据障碍类型规划越障行为。针对220 kV架空输电线路的结构特点,利用视觉传感器,设计了基于结构约束的障碍识别算法,完成了障碍识别和分类。根据障碍物的结构特点,设计了一种自适应多窗口区域立体匹配算法,实现了障碍物的双目视觉定位。模拟线路实验结果表明,算法能可靠地从复杂背景中识别并定位出防振锤、悬垂线夹和耐张线夹等障碍物,满足了巡线机器人导航要求。     

基于图像识别技术的核电文档智能化应用实践

本文对采用图像识别技术来辅助核电企业开展文档智能化应用进行研究。文章阐述了开展图像识别技术应用的业务背景、主要过程、实现原理和典型应用场景解决方案等内容,通过对基于图像识别技术的扫描文件清晰度的自动化检测以及基于光学图像文字识别技术的文件自动化拆分和比对这两个应用场景,阐述了面临的问题、解决方案的原理、具体的程序功能设计方案以及最终的应用效果。根据对应用效果的评估,证明了图像识别技术能够在文档智能化应用中发挥重要作用。通过本课题的研究和实践,为基于图像识别技术的文档智能化利用做了有益的探索。     

架空输电线路巡线机器人越障视觉伺服控制

为解决巡线机器人越障问题,设计了基于图像的越障视觉伺服控制方案．采用傅里叶描述子构造了具有平移、旋转、尺度缩放及起始点不变性的轮廓形状特征向量,实现了驱动轮的识别;进而抽取图像特征和估计驱动轮—相线在图像空间中的相对位姿,并设计了带有死区的比例控制律来实现驱动轮—相线“对中”视觉伺服控制．模拟线路实验结果表明,该方案能可靠地完成巡线机器人驱动轮—相线“对中”控制任务．     

Human eyebrow recognition in the matching-recognizing framework

This paper studies the problem of automatically recognizing human eyebrows using a frontal view. In the matching-recognizing framework for image-based object classification, we design an automatic human eyebrow recognition system via fast template matching and Fourier spectrum distance. Fast template matching is used to locate the target subregion of a gallery template or a pure eyebrow image in a probe original eyebrow image, whereas Fourier spectrum distance is used to determine the final identity of the probe original eyebrow image. We conducted a number of experiments to demonstrate the efficacy of the proposed system and corroborate the validity of eyebrow recognition on the BJUT eyebrow database. Moreover, we also tested the system on the color FERET database. Experimental results show that our approach can be directly applied to face recognition by only replacing eyebrow templates with face templates, and may achieve higher accuracy in eyebrow recognition than in small face recognition. This is a strong argument for eyebrow recognition to replace face recognition as an independent biometric in certain scenarios, especially where relatively large eyebrows can be cropped.     

Obstacle avoidance using flow field divergence

The use of certain measures of flow field divergence is investigated as a qualitative cue for obstacle avoidance during visual navigation. It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion. The necessary measurements can be robustly obtained from real image sequences. Experimental results are presented showing that the system responds as expected to divergence in real-world image sequences, and the use of the system to navigate between obstacles is demonstrated     

基于Cortex-M4的光电智能车路径识别最优化研究

研究基于Cortex-M4芯片的智能车路径识别最优化方案,选用光电传感器作为路径识别的传感器,结合路径识别算法和控制算法对智能车的坡道、速度和停车位等进行优化配置。制作的小型光电智能汽车,通过增加传感器和改进算法实现了路径识别,使小车循着赛道轨迹行进,在遇到坡道、障碍时能够自动作出响应。实际运行结果验证了所做研究的有效性。     

基于深度残差网络与迁移学习的毒蕈图像识别

我国毒蕈种类繁多且分布广泛,经常有人因无法鉴别毒蕈和可食用菌而误食毒蕈,导致身体健康甚至生命安全受到严重威胁。为了减少毒蕈中毒事件的发生,本文以中国常见毒蕈为研究对象,提出基于深度残差网络与迁移学习的毒蕈图像识别方法。首先通过互联网途径获取常见种类的毒蕈和非毒蕈的图像,经筛选后得到18种毒蕈和5种非毒蕈共14669张图像,使用数据增强扩充数据量,建立中国常见毒蕈图像数据集。然后以ResNet-152为预训练网络模型,采用基于模型的迁移学习方法,构建出毒蕈图像识别的模型结构,以Adam算法为模型优化方法,最后通过k折交叉验证进行模型训练。试验结果表明,毒蕈图像识别模型Top-1和Top-5准确率分别为92.17%和97.35%,对于常见毒蕈图像具有较高的识别率,可以有效的帮助人们避免误食毒蕈,为毒蕈识别研究提供新的方法。     

Human Face Recognition and the Face Image Set′s Topology

If we consider an n × n image as an n²-dimensional vector, then images of faces can be considered as points in this n²-dimensional image space. Our previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of this paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks