Asphalt concrete pavement early crack detection method and system

Through detection methods based on three-dimensional point cloud data and deep learning models, combined with texture analysis and machine learning models, the problem of difficult to detect and classify early cracks in asphalt concrete pavement in the existing technology is solved, efficient and accurate crack detection and early warning is achieved, and the safety and service life of the road are improved.

CN119810037APending Publication Date: 2025-04-11CHONGQING JIAOTONG UNIV
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Patent Information

Application Number
CN202411847691.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-16
Publication Date
2025-04-11

AI Technical Summary

Technical Problem

The prior art is difficult to efficiently and accurately detect and classify early cracks in asphalt concrete pavement, and lacks an effective early warning mechanism, which affects the safety and service life of the road.

Method used

The detection method based on three-dimensional point cloud data and deep learning model is adopted to identify suspected crack areas through texture analysis technology, and the crack segmentation and type identification are used using the Mask R-CNN model. The machine learning model is used to evaluate the development trend and damage degree of cracks, and a threshold is set for early warning.

Benefits of technology

Accurate detection and classification of early cracks on asphalt concrete pavement is achieved, detailed crack information is provided, efficiency and quality of pavement maintenance work is improved, and an effective crack status tracking and early warning mechanism has been established.

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Abstract

The invention discloses an asphalt concrete pavement early crack detection method and system, and the method comprises the steps: S1, obtaining pavement three-dimensional point cloud data to form a texture image, recognizing a gray level change region, and marking the region as a suspected crack region as a first crack degree; s2, segmenting a suspected crack region through a deep learning model Mask R-CNN, determining a crack type, and measuring geometric parameters of the crack as a second crack degree; s3, in combination with historical data and environmental factors, the development trend and the damage degree of the crack are evaluated through a machine learning model to serve as a third crack degree; s4, establishing a crack detection mechanism, and recording and tracking the state change of the early asphalt concrete pavement crack; and S5, setting and comparing crack state evaluation thresholds, and performing early warning when the crack state evaluation thresholds exceed a threshold boundary mechanism. According to the invention, an early crack state tracking and early warning mechanism is realized, and the efficiency and quality of pavement maintenance work are improved.
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