使用轻量级架构的高速模具结合质量检测 DSGβSI-SECS-Yolov7-Tiny
Bao Rong Chang1, Hsiu-Fen Tsai2, Wei-Shun Chang1
1Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
一个新的智能视觉检查模型,DSGβSI-SECS-YOLOv7-tiny,增强了对高速IC包装的压质量检测. 这种先进的模型提高了准确性和速度,降低了制造成本.
科学领域:
- 半导体制造业 半导体制造业
- 人工智能在质量控制中的作用
- 计算机视觉用于工业检查.
背景情况:
- 模具粘合对于IC包装的质量和产量至关重要.
- 提高自动化速度需要更快,更准确的视觉检查.
- 现有的检查方法与高速生产线相抗争,导致错误分类.
研究的目的:
- 开发一个高速的智能视觉检查模型,用于模具粘合.
- 提高分类准确度,并适应下一代自动化机械.
- 为了实现实时的过程参数调整,以提高产量和降低成本.
主要方法:
- 开发了DSGβSI-SECS-YOLOv7-tiny,是一种增强的轻量级模型.
- 集成深度可分离的卷积,幽灵卷积和可学习的Sigmoid激活.
- 集成的SE层,ECA-Net,协调注意力和小型对象增强器,以提高性能.
主要成果:
- 实现了294.1 FPS的推断速度.
- 达到99.1%的准确率.
- 与之前的DSGβSI-YOLOv7-tiny模型相比,表现出卓越的性能.
结论:
- DSGβSI-SECS-YOLOv7-tiny模型在高速压质量检查方面取得了重大进展.
- 该模型的效率和准确性支持实时缺陷检测和流程优化.
- 这项技术有望提高IC包装产量,减少制造损失.
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