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Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5
Liwei Wen1, Shihao Li1, Jiajun Ren2
1College of Material Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Materials (Basel, Switzerland)
|August 12, 2023
Summary
An improved YOLOv5 model enhances real-time surface defect detection in composite materials, boosting accuracy and speed. This reduces manual interventions and rework in automated tape laying and winding processes.
Area of Science:
- Materials Science
- Computer Vision
- Artificial Intelligence
Background:
- Automated composite material manufacturing faces challenges with surface defect detection, including low accuracy, slow speeds, and high error rates.
- Existing methods often require manual inspection, leading to increased costs and production delays.
- Real-time, high-precision defect detection is crucial for quality control in automated tape laying and winding.
Purpose of the Study:
- To develop an improved YOLOv5 algorithm for high-precision, real-time surface defect detection in composite materials.
- To reduce the need for manual intervention and rework during automated composite manufacturing processes.
- To enhance the performance of defect detection systems in terms of accuracy, speed, and reliability.
Main Methods:
- An enhanced YOLOv5 model incorporating Coordinate Attention (CA) mechanism for improved feature extraction.
- Integration of a Separate CA structure to optimize detection speed.
- Implementation of SIoU (SCYLLA-Intersection over Union) loss function to enhance target box regression stability.
- Utilizing Soft-SIoU-NMS for improved detection of overlapping defects, replacing traditional NMS.
Main Results:
- The improved YOLOv5 model achieved a mean average precision (mAP) of 97.2%, surpassing the 95% accuracy threshold.
- Detection speed increased to 72.1 frames per second (FPS), up from 66.7 FPS.
- The enhanced model demonstrated superior performance in detecting surface defects on pre-impregnated composite materials during automated processes.
Conclusions:
- The proposed improved YOLOv5 model effectively addresses limitations in current surface defect detection for composite materials.
- The integration of CA, SIoU loss, and Soft-SIoU-NMS significantly boosts detection accuracy and speed.
- This advancement facilitates real-time online detection, minimizing manual inspection and rework in automated manufacturing.
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