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YOLOv8-seg-CP:基于改进的YOLOv8-seg模型的芯片的轻量级实例细分算法
Zongjian Zhang1,2, Yanli Zou3,4, Yufei Tan1,2
1Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.
Scientific reports
|November 12, 2024
概括
本研究介绍了YOLOv8-seg-CP,这是一种用于实时芯片板检测和细分的轻量级算法. 它显著提高了准确性和速度,同时减少了用于自动芯片测试的模型大小.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 半导体制造业 半导体制造业
背景情况:
- 精确的芯片板检测和细分对于自动芯片对齐和位置校正至关重要.
- 现有的方法面临着小目标检测,细分精度和模型效率等挑战.
研究的目的:
- 开发一个轻量级和高效的芯片板实例细分算法.
- 为了提高小型芯片目标的检测和细分精度.
- 为了减少模型大小,计算负载和推断时间.
主要方法:
- 将StarNet集成到YOLOv8-seg骨干中,以改善功能捕获.
- 在子网络中开发C2f-Star模块,用于增强小目标特征提取.
- 引入轻型共享卷积细分头 (LSCSH) 和CGCAFusion模块,以改善细分和特征融合.
主要成果:
- 在芯片板数据集上实现了89.8%的检测和细分精度.
- 与基线相比,模型尺寸减少了45.6%,参数减少了50%,FLOP减少了31.7%.
- 每秒 (FPS) 提高了25.8%,推断时间减少了0.18ms.
结论:
- 拟议的YOLOv8-seg-CP算法在芯片板检测和细分方面提供了卓越的性能.
- 轻量级的设计和增强的特征提取有助于显著提高效率和准确性.
- 这种方法证明了自动芯片测试和分类的高工业应用价值.
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