In USA, a bridge should be inspected every two years according to AASHTO requirements. However, many in-service bridges are suffering from continuous deterioration and damages, requiring their conditions to be regularly monitored and assessed. Introductionīridge is a critical infrastructure. Finally, a case study of the Xiangjiang-River bridge inspection is carried out to verify the feasibility of bridge defects recognition based on this UAV system, achieving above 90% in the crack width recognition, which provides a better platform for bridge inspection. Then, the crack recognition method combining neural network and support vector machine is used to locate and extract the bridge cracks, and then, the actual cracks are calculated according to the optical principle. First, we have carried an evaluation experiment to determine the distance range of stable imaging for planning the safer bridge inspection route based on the special UAV system. Therefore, we have configured a bridge inspection UAV system with SLR camera, laser rangefinder. Considering the cost and inefficiency of the traditional method, the UAV system applied for bridge crack inspection is a better choice. Bridge defects are important indicator for the bridge safety assessment.
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