采算报查一体化的桥梁损伤检测方法及系统
By combining a two-stage decoupled head YOLO detection and segmentation model with an attention module and an unmanned aerial vehicle platform with a cloud platform, automated detection and quantitative calculation of bridge damage are achieved. This solves the problems of low efficiency, low safety, and difficulty in data visualization in existing bridge detection methods, improves detection accuracy and efficiency, and supports visualized analysis of bridge maintenance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHONGQING UNIV OF POSTS & TELECOMM
- Filing Date
- 2023-05-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing bridge damage detection methods suffer from problems such as low detection efficiency, insufficient robustness, low safety, easy data loss, and difficulty in visualization and analysis. In particular, they cannot effectively diagnose bridge damage in blind spots.
An improved YOLO detection and segmentation model with a two-stage decoupled head and attention module is adopted, combined with a UAV data acquisition platform and a cloud platform, to achieve automated detection and quantitative calculation of bridge damage. Through image processing technology and 3D surface reconstruction, combined with a cloud database, data management and visualization analysis are performed.
It improves the accuracy and efficiency of bridge damage detection, saves manpower and resources, ensures the safety of detection, and enables data visualization and statistical analysis, supporting the improvement of rural bridge maintenance and construction.
Smart Images
Figure CN116626059B_ABST