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RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8
Wenjie Tang1, Yangjun Deng1,2,3, Xu Luo1
1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Sensors (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces RST-YOLOv8, an improved chip surface defect detection algorithm that enhances accuracy for small defects. The model achieves high precision with reduced computational complexity, offering a lightweight solution for quality control.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Semiconductor Manufacturing
Background:
- Chip surface defect detection is critical for product quality and reliability.
- Existing methods struggle with low identification accuracy due to similar defect characteristics, small sizes, and scale variations.
Purpose of the Study:
- To develop an enhanced chip surface defect detection algorithm with improved accuracy and efficiency.
- To address challenges in identifying small, similar, and varied-scale defects.
Main Methods:
- Proposed RST-YOLOv8, an improved YOLOv8 algorithm incorporating the C2f_RVB module with RepViTBlock technology.
- Integrated SimAM attention mechanism for enhanced feature perception and a task-aligned dynamic detection head (TADDH) for improved small target detection.
Main Results:
- Achieved a 5.4% increase in mAP@0.5 on a chip surface defect dataset.
- Demonstrated significant improvements in detection accuracy and reduced computational complexity (parameters and GFLOPs).
- Outperformed other models in chip surface defect detection accuracy, balancing precision and lightweight design.
Conclusions:
- RST-YOLOv8 offers a significant advantage in chip surface defect detection accuracy.
- The model provides an optimal balance between computational resource consumption and real-time performance.
- Presents an ideal technical pathway for advanced chip surface defect detection tasks.
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