A spatio-temporal analysis method of pavement damage data based on multi-source feature fusion

Abstract

The invention relates to a spatio-temporal analysis method of pavement damage data based on multi-source feature fusion. Pavement damage detection adopts a semantic classification network to capture image data of local abnormalities and diseases of the pavement, and detect classification results of pavement abnormalities; GPS of damage data Positioning and video positioning space fusion, through the multi-section image mosaic algorithm, through the realization of multi-source image data fusion, establish a global road surface coordinate system; based on the damage data time series traceability based on shape matching, according to the road surface damage feature extraction under the time series, use space Based on the rough matching of location features, the matching of high-frequency and multiple-acquisition images is carried out at the same time; the construction of high-frequency data sets of natural road surface conditions, including high-frequency lightweight road surface state data collection, information extraction and fusion technology of multi-source data, and The data set interface is established. Compared with the prior art, the invention has the advantages of strong anti-interference ability, improved timeliness of road surface maintenance and management, and the like.

Description

technical field [0001] The invention relates to the technical field of road maintenance, in particular to a spatiotemporal analysis method of pavement damage data based on multi-source feature fusion. Background technique [0002] With the passage of time and the increase of the total number of facilities, the demand for highway maintenance in my country has shown a rapid growth trend. The development of large-scale maintenance work and the improvement of related technical research are inseparable from the support of comprehensive and accurate testing data. Although the overall evolution of the pavement state is long-term, the generation or development of meso-micro damage is sudden, the low-frequency detection is not time-sensitive, and it is difficult to observe the change process completely. It is necessary to pass high-frequency and time-sensitive detection data to ensure the accuracy of the identification of the development trend of the road surface. [0003] With the...

AI Technical Summary

Problems solved by technology

In addition, the current pavement state data still lacks a complete record of the influencing factors. The change of the pavement state is the result of the coupling of multiple factors such as traffic load and natural environment. A true and complete record of the change of each influencing factor is also a necessary condition for big data analysis.

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