An engineering site quality defect recording and inspection system and method based on AI visual recognition
By combining AI visual recognition technology with convolutional neural networks and Markov chains, the problems of inconsistent defect records and location misalignment in engineering quality inspections have been solved, achieving efficient and accurate defect identification and management while reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
The existing engineering quality inspection process suffers from problems such as inconvenient recording operations, high safety risks, low identification efficiency, duplicate defect registration, location misalignment, and increased management costs. Furthermore, the consistency of defect type identification and the inaccuracy of the location association module output are also issues.
An AI-based visual recognition system for recording and inspecting engineering site quality defects is adopted. Through a component semantic constraint module, a defect stability judgment module, an inspection location association module, and a defect state evolution module, combined with convolutional neural networks and Markov chain technology, the system achieves the binding of defect type identifiers with component location identifiers, the stability judgment of defect records, and the accurate location positioning, forming a unified set of engineering quality problem entries.
It improved the efficiency of defect discovery, reduced missed detections and duplicate dispatches, enhanced the consistency of defect type identification and the accuracy of defect location, and reduced management costs.
Smart Images
Figure CN122415530A_ABST