Intelligent corrosion analysis system and method based on deep learning
By using a CNN-LSTM model with a hybrid deep learning architecture, combined with data augmentation and temporal analysis, accurate identification and trend prediction of corrosion images are achieved, solving the problems of insufficient accuracy and efficiency in corrosion analysis in existing technologies, and providing an intelligent corrosion protection solution.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- MARINE TECHNOLOGY INNOVATION CENTER YANGTZE DELTA
- Filing Date
- 2026-02-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing corrosion analysis techniques are insufficient in terms of accuracy, efficiency, and predictability, making it difficult to meet the needs of modern industry for corrosion monitoring and protection, especially in terms of large-scale continuous monitoring and cross-environmental adaptability.
A hybrid deep learning architecture is adopted, combining a CNN-LSTM model. The CNN extracts the spatial features of the eroded image, and the LSTM analyzes the temporal pattern of erosion development. By combining data augmentation strategies and a temporal analysis module, the accuracy of erosion degree identification and trend prediction can be achieved.
It significantly improves the accuracy and predictive ability of corrosion identification, realizes fully automated corrosion analysis, supports large-scale detection needs, adapts to different application scenarios, and provides intelligent corrosion protection solutions.
Smart Images
Figure CN122415420A_ABST