Bridge safety monitoring method based on big data analysis and machine vision and cloud platform

Abstract

The invention discloses a bridge safety monitoring method based on big data analysis and machine vision and a cloud platform, and the method comprises the steps that the whole bridge floor of a large-span bridge is divided into a plurality of sub-regions, and the image obtaining and image preprocessing of the bridge floor of each sub-region is carried out through combining with high-definition cameras; meanwhile, feature extraction and road damage type matching are conducted on the bridge floor images of all the sub-regions, the depths of cracks and tracks in the bridge floor road damage types of all the sub-regions are detected, and then statistics is conducted on the comprehensive safety influence coefficient of the bridge, so that safety early warning of different levels is conducted and displayed. The method meets the requirement for rapid detection of bridge floor damage of the large-span bridge, improves detection efficiency and detection precision, reduces the cost of manpower and material resources, guarantees the safety of detection personnel, and greatly prolongs the service life of the bridge.

Description

technical field [0001] The invention belongs to the technical field of bridge safety monitoring, and in particular relates to a bridge safety monitoring method and a cloud platform based on big data analysis and machine vision. Background technique [0002] Cracks are one of the earliest forms of bridge deck problems, and only maintenance at the early stage of cracks can effectively improve the service life of bridge pavement. Therefore, the detection and identification of bridge pavement cracks is of great significance for improving the quality of bridge pavement. [0003] At present, due to the lack of research on the damage of long-span bridge decks in my country, there is a relatively lack of equipment for detecting bridge decks. Manual inspection requires the national bridge management department to allocate corresponding equipment, manpower and other resources to achieve regular safety inspections and inspections. . However, manual detection has the disadvantages of l...

AI Technical Summary

Problems solved by technology

[0003] At present, due to the lack of research on the damage of long-span bridge decks in my country, there is a relatively lack of equipment for detecting bridge decks. Manual inspection requires the national bridge management department to allocate corresponding equipment, manpower and other resources to achieve regular safety inspections and inspections.

However, manual detection has the disadvantages of low detection efficiency, safety problems of inspectors, and inability to guarantee detection accuracy, which cannot meet the rapid detection needs of long-span bridge deck damage.

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