As structures deteriorate and engineers age, efficient maintenance and management of civil engineering structures becomes increasingly important. Alternative inspection of civil engineering structures using images is being recognized as an efficient inspection method. However, acquiring images of large and extensive civil engineering structures is time and equipment-intensive, which hinders mobility and is consequently not efficient. In addition, the management of existing civil engineering structures is highly specialized and lacks scalability, which means that significant effort is required to associate images with civil engineering structures. Therefore, we decided to acquire images of large and extensive civil engineering structures with video cameras to improve mobility and efficiency. In addition, as a key to linking images and civil engineering structures, we developed a technology to record latitude and longitude information as audio data in a video using GNSS (Global Navigation Satellite System) + dual-frequency RTK (Real Time Kinematic) This facilitates the rapid comparison of images and management data for existing structures with latitude and longitude information, as well as image data management in chronological order. This results in improved productivity in image-based inspection. In addition, AI processing of image data has made it possible to sample deformed areas and analyze geographical characteristics. These combined technologies have raised expectations for preventive maintenance of structures using images.
GNSS, High resolution image, Mobile video camera, Multi camera, 360 degree camera, Civil engineering structure, Monitoring, AI, Preventive maintenance, Low cost, GIS, Image analysis
Railway Track Monitoring https://www.kk- nsg.co.jp/business/monitoring.html
Hsieh, C.-C., Hsu, T.-Y., & Huang, W.-H. (2022). An online rail track fastener classification system based on yolomodels. Sensors, 22(24).
Alvarenga, T. A., Carvalho, A. L., Honorio, L. M., Cerqueira, A. S., Filho, L. M. A., & Nobrega, R. A. (2021). Detection and classification system for rail surface defects based on eddy current. Sensors, 21(23).
Hsieh, C.-C., Lin, Y.-W., Tsai, L.-H., Huang, W.- H., Hsieh, S.-L., & Hung, W.-H. (2020). Offline deep- learning-based defective track fastener detection and inspection system. Sensors and Materials, 32(10), 3429.
Miwa. Numerical optimizers can bring about changes in railway maintenance.pdf In Japanese
Onishi, Mori, Oka, Sakakibara. Pavement Condition Assessment Powered by Machine-learning. Routes/Roads. Piarc-World Road Association(2021)
Naganuma. Development of monitoring technology for the efficiency and advancement of maintenance. Journal of the Japan Railway Facilities Association (3) (191-194) (2023) In Japanese
Working Group of the Temporary Digital Administrative Survey Committee (First Session) 2022.2.10 In Japanese
Patent 7148910, Japan, Sep 28, 2022, NAKASHA CREATIVE, Co. and CORE, Co.
Patent 7100863, Japan, Jul 6, 2022, NAKASHA CREATIVE, Co. and CONEXTIVO Inc.
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