电力巡检机器人终端视觉巡检技术分析

为了提升我国电网运行稳定性,推动智能电网建设发展,本文对电力巡检机器人终端视觉巡检技术应用进行探索分析,介绍了电力巡检机器人终端视觉巡检技术内容,包括环境感知网络、巡检路线识别等技术,随后结合实际实验,对技术实践进行了分析,希望为相关研究提供一定的参考。

有价票据视觉复检自动化包装系统设计

目的针对现有有价票据包装工艺中自动化程度低、人力成本高、分包效率低、出错率高的现象,设计一套视觉复检自动化分包系统。方法采用自动分拣机构实现票据高速分机和整理,并通过视觉检测技术检测票据编码的正确性;利用双工位机械臂和包扎机构实现快速包扎和整理。结果所设计系统分拣速度达800张/min,工作节拍可达80捆/min,最大限度地提高了系统工作效率。结论该设计为各类票据高速、高效包装系统的实现提供了参考。

智能电网巡检机器人终端视觉巡检技术研究

传统的变电站巡检主要是人工巡检,工作量繁多且不能实时检测到系统的反常和障碍,使得巡检存在漏检和效率低的问题。因此提出智能电网巡检机器人终端视觉巡检方法,使用3×3邻域的边缘检测算法,提取引导线的边缘特征点,依据边缘特征点通过隔10行扫描获取指定行像素的中点,得到引导线中心线,采用优化的行列联合搜索算法获取循迹图片中心线,使用"区域均衡"判别算法对图像进行判别,得到最佳引导线。依据最佳引导线采用智能电网巡检机器人终端视觉巡检控制系统,实现智能电网巡检机器人终端视觉巡检。实验结果表明,利用所提方法进行巡检控制的成功率在93%以上,具有高效率、抗干扰的优势。

电力巡检机器人终端视觉巡检技术

电力巡检机器人终端视觉检测技术运用在变电站机器人自动巡检当中,能够提升巡检效果,快速找到故障区域,并能发现人类肉眼无法发现的故障隐患。

高压输电线路的视觉巡检方法研究

高压输电线路视觉巡检系统的研究对保障输电线路安全稳定运行具有重要意义,而障碍物的检测识别是高压输电线路视觉巡检系统实现的关键技术之一。现针对高压输电线路的结构特点,提出了一种基于计算机图片处理技术的障碍物检测识别方法,该方法利用图像的边缘信息,采用直线、圆、椭圆检测方法,提取图像中直线、圆等几何基元,检验几何基元之间或几何基元和相线之间的结构关系来判断是否为线路障碍。

高压输电线路的视觉巡检方法研究

如何保障高压输电线路的持续、稳定运行一直以来都是电力企业的工作重点。因为高压输电线路的线路长、覆盖面积广等特征, 导致线路的巡检工作难度相对比较高。在巡检中采取视觉巡检方式,可以实现对线路的高质量、及时性的巡检,可以及时发现障碍物并实 现对障碍物特征的分析,从而保障线路的运行质量。本文基于当前的高压输电线路结构特征,探讨一种高质量、高效率以及可靠性的障碍 物检测识别技术,可以通过图像边缘信息、不同形状的检测方式实现图像中集合单元的提取,并借助图形关系进行识别判断是否属于线路 障碍物。希望可以借助本文为相关从业者提供帮助。

基于TRIZ理论解决轨道式矿用巡检机器人应用场景受限的问题

使用多种TRIZ理论将“轨道式矿用巡检机器人”创新为“转向履带式双目视觉矿用巡检机器人(机械臂款)”,该机器人可自动识别视觉对象,分析视觉对象位姿信息及机械臂执行端位姿信息,可手眼协调操作矿山相关机械,有效降低人工成本,提高矿山作业巡检、管理、日常操作和临危操作的智能化和无人化水平。

Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

Contrast Detection Learning Improves Visual Contrast Sensitivity of Cat

Psychological studies on human subjects show that contrast detection learning promote learner＇s sensitivity to visual stimulus contrast. The underlying neural mechanisms remain unknown. In this study, three cats （Felis catus） were trained to perform monocularly a contrast detection task by two-altemative forced choice method. The perceptual ability of each cat improved remarkably with learning as indicated by a significantly increased contrast sensitivity to visual stimuli. The learning effect displayed an evident specificity to the eye employed for learning but could partially transfer to the naive eye, prompting the possibility that contrast detection learning might cause neural plasticity before and after the information from both eyes are merged in the visual pathway. Further, the contrast sensitivity improvement was evident basically around the spatial frequency （SF） used for learning, which suggested that contrast detection learning effect showed, to some extent, a SF specificity. This study indicates that cat exhibits a property of contrast detection learning similar to human subjects and can be used as an animal model for subsequent investigations on the neural correlates that mediate learning-induced contrast sensitivity improvement in humans.

Visual Identification Method and Implementation of Impurities in Food Grain

A method to check impurities in food grain based on digital image processing is proposed in this study. According to the feature of the impurity, one image with twice processing is put forward based on blob analysis, which eliminates impurities that were brighter or darker than normal particles. With the image processing system developed by Matrox Imaging Library, black and white blobs are extracted for twice with one function, and then the size and the position of impurities are calculated quickly through mathematical morphologic and blob analysis. The method can be extended and applied in other fields due to its advantages of quick speed and high accuracy.

Eye detection and tracking using IR source

A real-time prototype of drowsy driver monitoring system using infrared sources to detect and track the eyes is presented. A special hardware is designed for controlling the LEDs and real-time image acquisition. This method adopts the differential image to enhance the accuracy and robustness of eye detection. A Kalman filter is implemented to track the eyes. The experimental results are discussed. The system is non-invesive, fast and reasonably accurate.

Design of a road vehicle detection system based on monocular vision

In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.

Computer vision technology in log volume inspection

Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardware system was analyzed and the details of the inspection algorithms were given. A fuzzy entropy based on image enhancement algorithm was presented for enhancing the image of the cross-section of log. In many practical applications the cross-section is often partially invisible, and this is the major obstacle for correct inspection. To solve this problem, a robust Hausdorff distance method was proposed to recover the whole cross-section. Experiment results showed that this method was efficient.

NEW CORNER DETECTION ALGORITHM BASED ON MULTI-FEATURE SYNTHESIS

Corner detection is a chief step in computer vision. A new corner detection algorithm in planar curves is proposed. Firstly, from the human perception, two key characteristics are given as an amendment of the traditional corner properties. Based on the two properties, the concept of the fuzzy set is introduced into a detection. Secondly, the extracted-formulae of three groups including the features of the corner subject degree are derived. Through synthesizing the features of three groups, the judgments of the corner detection, location, and optimization are obtained. Finally, by using the algorithm the detection results of several examples and feature curves for some interested parts, as well as the detection results for the test images history in references are given. Results show that the algorithm is easily realized after adopting the fuzzy set, and the detection effect is very ideal.

气缸盖定位密封套组合件装配方法的研究应用

缸盖尤顺定位套及密封圈用于尤顺制动系统摇臂座与缸盖间的定位以及制动油路的通行。该定位套及密封圈目前在装配中,由人工将定位套及密封圈进行组合,安装到缸盖相应的孔内,在少人化人员配置条件下影响生产节拍,存在漏装、密封圈挤伤等质量问题。本文针对该定位套及密封圈组合的全自动压装进行研究,经过方案对比,确定最经济、高效的实施方案,分别使用自动筛料、自动组合、自动扫码及压装、视觉检测防错等实现全自动压装。

Vision testing method for diameter,roundness and hardness of table tennis ball

Under the premise of fully respecting current national standard of table tennis ball,this paper presents an integrated and effective method for testing diameter,roundness and hardness of table tennis ball based on vision measurement. Firstly,camera calibration is performed by 40 mm gauge block to capture the images of table tennis ball in static and pressed state in an appropriate lighting condition. Then image binarization and extraction of image contour are carried out. The least square method is used to fit its center. Finally,the diameter,roundness and hardness are calculated. The measurement data are analyzed by using the method presented in this paper for table tennis ball with known parameter data and with unknown parameter data. Experimental results show that this method can improve measurement accuracy and provide a technical basis for the online testing and integration measurement of table tennis ball.

Hand-eye-vision based control for an inspection robot's autonomous line grasping

In order to ensure that the off-line arm of a two-arm-wheel combined inspection robot can reliably grasp the line in case of autonomous obstacle crossing,a control method is proposed for line grasping based on hand-eye visual servo.On the basis of the transmission line's geometrical characteristics and the camera's imaging principle,a line recognition and extraction method based on structure constraint is designed.The line's intercept and inclination are defined in an imaging space to represent the robot's change of pose and a law governing the pose decoupling servo control is developed.Under the integrated consideration of the influence of light intensity and background change,noise(from the camera itself and electromagnetic field)as well as the robot's kinetic inertia on the robot's imaging quality in the course of motion and the grasping control precision,a servo controller for grasping the line of the robot's off-line arm is designed with the method of fuzzy control.An experiment is conducted on a 1:1 simulation line using an inspection robot and the robot is put into on-line operation on a real overhead transmission line,where the robot can grasp the line within 18 s in the case of autonomous obstacle-crossing.The robot's autonomous line-grasping function is realized without manual intervention and the robot can grasp the line in a precise,reliable and efficient manner,thus the need of actual operation can be satisfied.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Vision-based long-distance lane perception and front vehicle location for full autonomous vehicles on highway roads

A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.

OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO

Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.