基于高效的Yolo网络和增量学习的云端协作缺陷检测
Zhenwu Lei1, Yue Zhang1, Jing Wang1
1The School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了SGRS-YoloV5n,这是一种用于工业缺陷检测的轻量级深度学习模型. 它提高了边缘设备的准确性和实时性能,解决了新的缺陷类别带来的挑战.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 深度学习缺陷检测模型难以扩展到新类别,并在资源有限的边缘设备上实现实时性能.
- 现有的轻量级模型往往没有足够的检测准确性,用于工业应用.
研究的目的:
- 介绍一个新的轻量级深度学习模型,SGRS-YoloV5n,用于增强边缘设备的缺陷检测.
- 开发一个云端的协作系统,以增量学习来提高准确性和适应性.
主要方法:
- 将四个模块 (SCDown,GhostConv,RepNCSPELAN4,ScalSeq) 集成到YoloV5架构中,以创建SGRS-YoloV5n.
- 构建一个云端协作系统,用于分层缺陷检查.
- 实施增量学习机制,以适应性学习新的缺陷类别.
主要成果:
- 与现有的轻型模型相比,SGRS-YoloV5n显示出更高的检测精度和实时性能.
- 该模型显著提高了特征提取和计算效率,同时减少了模型大小和负载.
- 云端系统有效地提高了整体检测准确性和效率.
结论:
- SGRS-YoloV5n是资源有限的工业环境中实时缺陷检测的有价值和稳定的解决方案.
- 拟议的云端协作系统与增量学习提供了一种新的方法,以高效和准确的缺陷检测.
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