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Published on: September 2, 2019
Research on glass surface defect detection method based on shadowgraphy imaging and improved YOLOv11n
Yulu Xue1, Yang Li2, Hongbin Chen1
1Beijing Institute of Graphic Communication, Beijing, 102600, China.
Scientific Reports
|June 4, 2026
Summary
This study introduces YOLO-FSA, an improved algorithm for detecting glass surface defects using shadowgraphy. It enhances detection accuracy and efficiency while reducing model size and computational load.
Area of Science:
- Materials Science
- Computer Vision
- Artificial Intelligence
Background:
- Defect detection on glass surfaces is challenging due to the subtle nature of flaws.
- Existing methods often struggle with efficiency and accuracy for elongated or faint defects.
Purpose of the Study:
- To develop an efficient and accurate defect detection method for ordinary glass surfaces.
- To improve the detection of faint and elongated defects using a novel algorithm.
Main Methods:
- A glass surface defect dataset was created using shadowgraphy technology.
- The YOLO-FSA algorithm, based on YOLOv11n, incorporates C3k2_FWD modules and a GSCSA attention mechanism.
- Down convolution was used for model lightweighting.
Main Results:
- YOLO-FSA achieved precision of 74.8%, recall of 80.7%, and mAP50 of 85.5%.
- Significant improvements were observed compared to the base YOLOv11n model (8.5% precision, 6.4% recall, 12.6% mAP50 increase).
- Model parameters were reduced by 31.4% and computational complexity by 25.4%.
Conclusions:
- The YOLO-FSA algorithm effectively balances high detection performance with computational efficiency.
- This method offers a promising solution for automated, high-accuracy defect detection in glass manufacturing.
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