基于深度学习的桥梁裂缝特征识别模型构建方法
By constructing a bridge crack feature recognition model based on deep learning, and utilizing image feature analysis and a critical discrimination feature library, the problem of misjudgment of water surface wetting traces in bridge crack recognition was solved, and efficient and accurate recognition was achieved under multi-light conditions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN ZHONGBO TIANCHENG CONSTRUCTION ENGINEERING CO LTD
- Filing Date
- 2025-10-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing bridge crack feature identification methods are prone to misidentifying water-soaked marks as damp cracks when identifying bridge cracks that are close to the water surface, leading to inaccurate identification results.
A bridge crack feature recognition model based on deep learning was constructed. Crack image features and wet gray values were obtained through image feature analysis. A critical distinguishing feature library was established. Deep learning was used to store features under various lighting conditions, and a crack recognition model was constructed for recognition.
It improves the accuracy of identifying cracks close to the water surface in bridges, and can effectively store and identify the characteristics of damp cracks and water seepage traces under various lighting conditions, thus improving identification efficiency and accuracy.
Smart Images
Figure CN121305180B_ABST
Abstract
Citation Information
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