Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
Source abstract
Abstract
Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net algorithm that utilizes images taken by unmanned vehicles. Unlike the U-Net algorithm, however, this algorithm identifies the optimal function by setting the epoch to 120 and uses the Adam optimization algorithm. In addition, a bilateral filter was applied to highlight the damaged areas of the bridge, and a different color was used for each of the five types of abnormalities detected, such as cracks. Next, we trained and tested 135,696 images of exterior bridge damage, including concrete delamination, water leakage, and exposed rebar. Through the analysis, we confirmed an analysis method that yields an average inspection reproduction rate of more than 95%. In addition, we compared and analyzed the inspection reproduction rate of the method with that of BIRD U-Net after using the same method and images for training as the existing U-Net and ResNet algorithms for validation. In addition, the algorithm developed in this study is expected to yield objective results through automatic damage analysis. It can be applied to regular inspections that involve unmanned mobile vehicles in the field of bridge maintenance, thereby reducing the associated time and cost.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Structural Health Monitoring Techniques · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center for Science and Technology Policy and Convergence, Ajou University, Suwon 16499, Republic of Korea
Neongkul Mobile, Suwon 16573, Republic of Korea
Department of Industrial Engineering, Ajou University, Suwon 16499, Republic of Korea
Funding
Science and Technology Policy Expert Development and Support Program through the Ministry of Science and ICT of Korean government
References (27)
Show source-provided references
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