使用GLV-YOLO的光电子探测器的缺陷检测算法
Xinfang Zhao1, Qinghua Lyu1, Hui Zeng1
1National "111 Research Center" Microelectronics and Integrated Circuits, School of Science, Hubei University of Technology, Wuhan 430068, China.
Micromachines
|March 27, 2025
概括
本研究介绍了一种优化的GLV-YOLO模型,用于实时光电探测器表面缺陷检测. 改进的算法实现了高精度,降低了复杂性,改善了制造质量控制.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机视觉 计算机视觉
背景情况:
- 光探测器是各种应用中的关键组件.
- 精确的表面缺陷检测对于光电探测器的制造和性能至关重要.
- 现有的方法可能缺乏工业环境所需的实时处理和精度.
研究的目的:
- 为实时和准确的光电探测器表面缺陷检测开发一个优化的算法.
- 提高制造环境中缺陷检测模型的效率和性能.
主要方法:
- 为光电探测器缺陷检测开发了一个优化的GLV-YOLO模型.
- 整合了GhostC3_MSF模块以减少模型复杂性和参数数量.
- 集成了LSKNet_3注意力机制,用于增强特征提取.
- 利用WIoU损失函数通过最小化几何惩罚来提高概括性能.
主要成果:
- 拟议的算法实现了98.9%的准确性.
- 该模型有210万个参数,计算成本为7.0 GFLOPs.
- 与现有方法相比,证明了更高的性能和效率.
结论:
- 优化的算法有效地满足了光电探测器制造中的实时和精确缺陷检测需求.
- 整合GhostC3_MSF,LSKNet_3和WIoU显著提高了模型性能和效率.
- 这种方法有助于提高质量控制和光探测器技术的进步.
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