RST-YOLOv8:基于YOLOv8的改进芯片表面缺陷检测模型
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
概括
这项研究引入了RST-YOLOv8,这是一种改进的芯片表面缺陷检测算法,可以提高小缺陷的准确性. 该模型以降低计算复杂度实现高精度,为质量控制提供轻量化解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 半导体制造业 半导体制造业
背景情况:
- 芯片表面缺陷检测对于产品质量和可靠性至关重要.
- 现有的方法由于类似的缺陷特征,小尺寸和尺度变化而难以识别,识别准确度低.
研究的目的:
- 开发一个增强的芯片表面缺陷检测算法,以提高准确性和效率.
- 解决识别小,相似和不同规模缺陷的挑战.
主要方法:
- 提出了RST-YOLOv8,这是一个改进的YOLOv8算法,结合了C2f_RVB模块与RepViTBlock技术.
- 集成的SimAM注意力机制,用于增强特征感知,以及任务调整的动态检测头 (TADDH),用于改进小目标检测.
主要成果:
- 在芯片表面缺陷数据集上实现了mAP@0.5的5.4%的增加.
- 在检测准确度和减少计算复杂度 (参数和GFLOPs) 中显著改进.
- 在芯片表面缺陷检测精度,平衡精度和轻量级设计方面表现优于其他型号.
结论:
- 在芯片表面缺陷检测准确度方面,RST-YOLOv8具有显著的优势.
- 该模型提供了计算资源消耗和实时性能之间的最佳平衡.
- 为先进的芯片表面缺陷检测任务提供了理想的技术途径.
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