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Received: 15 July 2021 / Revised: 26 July 2021 / Accepted: 26 July 2021 / Published: 30 July 2021
Simple bridge motion analysis using video cameras is effective in determining the optimal control of multiple railway structures through condition-based maintenance. Although the size environment affects the size of non-labeled images well, the value of measuring the variable size of non-labeled images and the effect of different environments is not well understood. In this study, the accuracy of unmarked image tracking and the effect of light is demonstrated using an example fabric, and the accuracy and applicability are discussed. Also, field tests on two bridges – steel and solid bridges – on low- and high-speed railways confirm the accuracy and practical application of non-marked image measurements in the environment . This displacement was observed to be measured with an accuracy of ~1/30 pixel (error of ~0.4 mm at 20 m position) in daylight and full brightness. Also, the basis for the subset position and post-processing methods are discussed to ensure the equalization of the non-linear image on the rigid bridge with low contrast.
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Image size; move around; railways; no sign; television viewing; move around; railways; no sign; field test
There is a long history of railway construction, and existing railway bridges were built more than 100 years ago [1]. In addition, in developed countries, there has been a reduction in the number of personnel responsible for these bridges. Therefore, there is a need for improvement in the performance of bridge management systems based on real-time monitoring. In particular, a shift to condition-based maintenance (CBM) is proposed. CBM aims to improve the performance of maintenance and safety by accurately evaluating the operational performance of railway bridges and improving the type and time of system implementation [2, 3]. Accurate and quantitative analysis of bridge performance based on measurement is important for understanding CBM. One of the proven properties of railway bridges is the deflection (horizontal deflection) under the railway crossing [4, 5]. However, a traditional contact-type contact or wire-type method is required to work on a large surface and it is not possible depending on the conditions under the bridge (for example, a bridge). Due to this problem, many developments of simple modeling techniques have been conducted [6, 7, 8]. This study contributes to the implementation of CBM from the point of view of improving the performance of the measurement system by focusing on the replacement process of the bridge through image processing.
The concept of this study for image displacement measurement is based on the idea that it will be used for the inspection and visual inspection of existing bridges. This point is different from previous studies that aimed to develop a more accurate measurement system. Therefore, non-marked imaging that uses only minimal imaging equipment (i.e., a camera and only a tripod) and does not require access to bridges will be considered in this study. The idea is to understand the first research based on the performance of the bridge by creating a simple observation method that can be applied in the current research and patrol process. In addition, statistics are kept of organizational performance and daily patrol observations, which provide important data and objectives for future management decisions and long-term management of the bridge.
The quality of measurement and the applicable conditions are critical issues for this type of simple measurement system. In this study, a laboratory test model [9] confirmed the accuracy and brightness conditions for non-linear image measurements. The applicable range was determined based on the bending data of the railway bridge during the train journey. Also, field tests using two bridges, on the low and high railway, show the effectiveness of non-linear image measurement in real world conditions.
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In this paper, Section 2 reviews related works and Section 3 describes non-linear image measurement techniques. Section 4 describes the results of the preliminary research using the model bridge and the calculation results of the applicable parameters. Section 5 presents results from the application on two bridges and other materials.
Due to the progress in the cost and power of video, such as the development of speed and high resolution, images using video cameras are gradually becoming more common. Because displacement at the railway station is the main structural design, in this section the major contributions of the past are reviewed, with emphasis on the measurement of bed changes and the speed with the use of images.
This system measures the bridge’s motion using images and long-distance signals. Lee and Shinozuka [10] reported the actual inspection of a bridge using an industrial video camera in 2006. Finally, many improvements and applications of bridge reconstruction methods have been made using industrial video cameras. For example, Choi et al. [11] proposed a method to measure the dynamic deflection of bridges using hand-held video recordings through an image processing technique that changes the region of interest and updates the statistics. Ribeiro et al. [12] established a monitoring system for railway bridges using industrial video cameras. They proposed a method using dedicated markers to track parallel positions in images obtained by digital image integration (DIC). Also, the lights on the windshield are used to ensure adequate lighting and limit camera disturbance. In the case of video monitors where accuracy is a problem, their method achieves a maximum resolution of 0.1 mm (shooting from a distance of 10 m). Cigada et al. [13] investigated the effect of camera type, image processing method, and enhancement level on an image-based method to estimate bridge deflection during train crossings. They noted that a significant increase in measurement error is inevitable in non-significant variable measurements. Feng et al. established a monitoring system for railway bridges through several studies using industrial video cameras [14, 15, 16]. Moreover, accuracy is clearly confirmed, not only when using markers, but also when considering displacement without markers [16]. They proposed a sub-pixel projection method for high-resolution visualization [15]. Pan et al. proposed a DIC-based image decoding method for railway bridges [17]. Here, a computer-controlled industrial video camera is proposed for use in signal-free displacement measurements with a time frame that allows the results to be confirmed at the measurement site.
Furthermore, many studies are active in interventions related to the measurement of continuous image changes, as described in two research papers [18, 19]. Feng and Feng (2018) summarized the importance of multipoint measurement data obtained from images from damage detection [18]. Also, Xu and Brownjohn (2018) discussed to evaluate the structural health and the difficulties to be solved in the future for each elemental technology (camera calibration, target tracking, and structural displacement) [19].
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Recently, Ribeiro et al. [20] obtained a remote image measurement system using an aerial vehicle. Automation and ease of imaging using aerial vehicles and robots has great potential to simplify the inspection and inspection of buildings, which is the basis of this study. Although the use of air vehicles is an important research topic, it is outside the scope of this study and future work.
Most of the studies mentioned above confirmed their measurements on real bridges after full confirmation of their accuracy in the laboratory. Therefore, there are a few moments in which the speed limit is considered when you understand the limits of the camera’s distance and the environmental conditions under consideration, in terms of both the data is not measured. However, the quantification of the relationship between camera distance and weight ratio performed by Ribeiro et al. [12] is a significant contribution. To determine whether the image size
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