A glass cover plate surface defect detection and sorting method, device and system
By improving the backbone and neck networks of the YOLOv1 model, and combining data augmentation technology with robotic arm sorting, the problem of balancing detection accuracy and speed in glass cover surface defect detection and sorting was solved, achieving efficient automated detection and sorting and reducing labor costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FOSHAN UNIVERSITY
- Filing Date
- 2026-02-25
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for detecting and sorting defects on the surface of glass covers suffer from problems such as difficulty in balancing detection accuracy and speed, complexity and diversity of defect types, and insufficient system generalization ability. Furthermore, relying on manual quality inspection results in low efficiency and accuracy is affected by human factors.
The YOLO-MDI model is used to detect defects on the surface of glass covers. By improving the backbone and neck networks of the YOLOv1 model, introducing the MSECA attention mechanism and dynamic upsampling layer, and combining data augmentation technology and robotic arm sorting, automated detection and sorting are achieved.
It improves detection accuracy and real-time performance, significantly reduces labor costs, and enhances detection efficiency and product quality consistency.
Smart Images
Figure CN122415418A_ABST