Catenary is an important infrastructure along electric railways, which should be periodically checked to guarantee the normal working status of power supply for the trains. Technologies of ultraviolet detection has been introduced into catenary inspection, and it can be used to detect the electrical state for insulators that are the isolation components between high voltage and low voltage in catenary. However, due to the dynamic detection condition with the running of catenary inspection car, the performance of multi-channel image fusion becomes a key issue in the application on electric railways. In this paper, we proposed a multi-channel image fusion method for the ultraviolet and visible image in dynamic detection on electric railways. The ultraviolet detection module is designed with dual optical coupling alignment, which guarantees the consistency of multi-channel in spatial domain. The post detection is adopted and used as an external trigger signal for both the ultraviolet and visible cameras, which are working in triggered acquisition mode with good time synchronization. Finally, insulator localization algorithms based on deep learning are utilized in the automatic detection of insulator area in visible image, and the ultraviolet signals are successfully extracted from the image fusion results. The performance of multi-channel image fusion method proposed in the paper has been validated, in both lab test and dynamic test on electric railways
The angle of steady arm is an important inspection parameter of catenary. The existing manual measurement methods were unable to meet the requirements on measurement efficiency or measurement accuracy, which greatly restrict the efficiency of defects inspection and maintenance of catenary on high-speed railways. An automatic visual measurement method with excellent measurement efficiency was developed for the angle of steady arm, which can be used on catenary inspection car. However, the inspection reliability is poor for images with complicated background, such as images at the posts with multiple cantilever and between two posts with no steady arms. In order to solve the problem on system reliability, an upgraded visual measurement method is proposed in the paper. The camera system is changed into trigger image acquisition mode with a post detection module integrated, and the image detection algorithm for steady arm is greatly improved using deep convolutional neural networks, exploiting the research progress on object detection for catenary component. The proposed system has been fully tested on detection reliability, measurement repeatability and measurement accuracy, which shows much better reliability and availability.
Railway patrolling inspection train has been widely used for railway infrastructure safety monitoring. Cameras are mounted on the train, which can capture the image of the overhead contact power line system for defect detection. In the catenary support device of overhead contact power line system, the insulator can keep the catenary equipment insulated from other equipment. Defect detection of insulators is extremely important to railway safety. In recent years, some achievements have been made in defect detection on railway system based on computer vision. We propose an insulator localization algorithm and insulator defect detection algorithm using deep convolutional neural networks. Firstly, the insulator localization network based on Rotation Region Proposal Network (RRPN) can be used to locate insulator area in catenary support device images by using rotated bounding box. Rotated bounding box can effectively eliminate unnecessary background in localization results. After that, based on the insulator localization results, a Faster R-CNN based insulator defect detection network was used to detect defect of insulator. This method can effectively detect defect of insulator and solve the high false positive defect problem.
Catenary geometry measurement is one of the main inspection fields to evaluate the infrastructure quality and status of power supply system on high-speed railways. Existing measurement methods have disadvantages of complex system architecture, expensive device cost, or sensitive to environment changes. In this paper, a new measurement approach is proposed which is based on infrared image processing. The system architecture is compact in hardware, and environment changes have no effects on the measurement results. Moreover, the infrared images are shared by thermal cameras from other inspection systems, which means a low device cost of the system. The proposed method has been successfully used on the comprehensive inspection train, which shows the advantages in infrastructure inspection on high-speed railways.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.