Application of Computer Vision Technology to Structural Health Monitoring of Engineering Structures

2019 ◽  
pp. 256-268 ◽  
Author(s):  
X. W. Ye ◽  
T. Jin ◽  
P. Y. Chen
Keyword(s):  
Computer Vision ◽  
Health Monitoring ◽  
Electromagnetic Interference ◽  
High Speed ◽  
Low Noise ◽  
Bridge Inspection ◽  
Engineering Structures ◽  
Computer Vision Technology ◽  
Aerial Vehicle ◽  
Railway System

The computer vision technology has gained great advances and applied in a variety of industry fields. It has some unique advantages over the traditional technologies such as high speed, high accuracy, low noise, anti-electromagnetic interference, etc. In the last decade, the technology of computer vision has been widely employed in the field of structure health monitoring (SHM). Many specific hardware and algorithms have been developed to meet different kinds of monitoring demands. This chapter presents three application scenarios of computer vision technology for health monitoring of engineering structures, including bridge inspection and evaluation with unmanned aerial vehicle (UAV), recognition and surveillance of foreign object intrusion for railway system, and identification and tracking of concrete cracking. The principles and procedures of three application scenarios are addressed following with the experimental study, and the possibilities and ideas for the application of computer vision technology to other monitoring items are also discussed.

High Precision Automatic Alignment and its Computer Vision Technology

1989 ◽  
Vol 1 (3) ◽  
pp. 220-226
Author(s):  
Tohru Tanigawa ◽  
◽  
Toshitsugu Sawai ◽  
Tadashi Nakao
Keyword(s):  
Computer Vision ◽  
High Precision ◽  
Flexible Manufacturing ◽  
High Speed ◽  
Manufacturing Systems ◽  
Ultraviolet Rays ◽  
Human Ability ◽  
Automatic Alignment ◽  
Computer Vision Technology ◽  
Application Fields

Recently, industrial robotics and computer vision technology has become very important in flexible manufacturing systems and automated factories. Especially high precision automatic alignment technology beyond human ability is essential to some manufactures, and its application fields are extending rapidly. This paper describes the high precision automatic alignment system of large-sized LCD panels. The features of the system are (1) high precision and high speed detection of position using the special alignment mark, (2) high contrast image obtained by the use of ultraviolet rays, (3) new image-processing algorithms for improvement of system reliability.

scholarly journals Motion Control System of Unmanned Railcars Based on Image Recognition

2019 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Yuan-Wei Tseng ◽  
Tsung-Wui Hung ◽  
Chung-Long Pan ◽  
Rong-Ching Wu
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Motion Control ◽  
Image Recognition ◽  
Electromagnetic Interference ◽  
Integrated System ◽  
Radio Communication ◽  
Motion Control System ◽  
Computer Vision Technology ◽  
And Control

The main purpose of this paper is to construct an autopilot system for unmanned railcars based on computer vision technology in a fixed luminous environment. Four graphic predefined signs of different colors and shapes serve as motion commands of acceleration, deceleration, reverse and stop for the motion control system of railcars based on image recognition. The predefined signs’ strong classifiers were trained based on Haar-like feature training and AdaBoosting from Open Source Computer Vision Library (OpenCV). Comprehensive system integrations such as hardware, device drives, protocols, an application program in Python and man machine interface have been properly done. The objectives of this research include: (1) Verifying the feasibility of graphic predefined signs serving as commands of a motion control system of railcars with computer vision through experiments; (2) Providing reliable solutions for motion control of unmanned railcars, based on image recognition at affordable cost. The experiment results successfully verify the proposed methodology and integrated system. In the main program, every predefined sign must be detected at least three times in consecutive images within 0.2 s before the system confirms the detection. This digital filter like feature can filter out false detections and make the correct rate of detections close to 100%. After detecting a predefined sign, it was observed that the system could generate new motion commands to drive the railcars within 0.3 s. Therefore, both real time performance and the precision of the system are good. Since the sensing and control devices of the proposed system consist of computer, camera and predefined signs only, both the implementation and maintenance costs are very low. In addition, the proposed system is immune to electromagnetic interference, so it is ideal to merge into popular radio Communication Based Train Control (CBTC) systems in railways to improve the safety of operations.

scholarly journals Coal and Gangue Separating Robot System Based on Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1349
Author(s):  
Zhiyuan Sun ◽  
Linlin Huang ◽  
Ruiqing Jia
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
High Speed ◽  
Robot System ◽  
Use Of Resources ◽  
Planning Algorithm ◽  
Computer Vision Technology ◽  
Conveyor Belts ◽  
The Difference ◽  
Save Energy

In coal production, the raw coal contains a large amount of gangue, which affects the quality of coal and pollutes the environment. Separating coal and gangue can improve coal quality, save energy, and reduce consumption and make rational use of resources. The separated gangue can also be reused. Robots with computer vision technology have become current research hotspots due to simple equipment, are efficient, and create no pollution to the environment. However, the difficulty in identifying coal and gangue is that the difference between coal and gangue is small, and the background and prospects are similar. In addition, due to the irregular shape of gangue, real-time grasping requirements make robot control difficult. This paper presents a coal and gangue separating robot system based on computer vision, proposes a convolutional neural network to extract the classification and location information, and designs a robot multi-objective motion planning algorithm. Through simulation and experimental verification, the accuracy of coal gangue identification reaches 98% under the condition of ensuring real-time performance. The average separating rate reaches 75% on low-, medium-, and high-speed moving conveyor belts, which meets the needs of actual projects. This method has important guiding significance in detection and separation of objects in complex scenes.

Development of Wire Suspended Robot System (ARANEUS 2.0) for Bridge Inspection

10.29007/zw9k ◽  
2020 ◽  
Author(s):  
Kazuhide Nakata ◽  
Kazuki Umemoto ◽  
Kenji Kaneko ◽  
Ryusuke Fujisawa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Concrete Surface ◽  
Wire Length ◽  
High Definition ◽  
Bridge Inspection ◽  
Robot System ◽  
Inspection Method ◽  
Aerial Vehicle ◽  
The Wire

This study addresses the development of a robot for inspection of old bridges. By suspending the robot with a wire and controlling the wire length, the movement of the robot is realized. The robot mounts a high-definition camera and aims to detect cracks on the concrete surface of the bridge using this camera. An inspection method using an unmanned aerial vehicle (UAV) has been proposed. Compared to the method using an unmanned aerial vehicle, the wire suspended robot system has the advantage of insensitivity to wind and ability to carry heavy equipments, this makes it possible to install a high-definition camera and a cleaning function to find cracks that are difficult to detect due to dirt.

scholarly journals A Review on Computer Vision Technology for Monitoring Poultry Farm – Application, Hardware, and Software

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Nur Syazarin Natasha Abd Aziz ◽  
Salwani Mohd Daud ◽  
Rudzidatul Akmam Dziyauddin ◽  
Mohamad Zulkefli Adam ◽  
Azizul Azizan
Keyword(s):  
Computer Vision ◽  
Poultry Farm ◽  
Computer Vision Technology
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