基于YOLOv8n-GSS的轴承环表面缺陷检测方法
Shijun Liang1, Haitao Xu1, Jingyu Liu1
1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种改进的YOLOv8-GSS方法,用于工业轴承表面缺陷检测. 改进的模型显著提高了识别各种缺陷大小的准确性和速度,改善了制造质量控制.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 工业轴承表面缺陷检测受到复杂的背景,多尺度缺陷和不足的特征提取的挑战.
- 现有的方法在现实世界工业环境中经常与准确性和效率作斗争.
研究的目的:
- 为工业轴承表面提出一个改进的YOLOv8-GSS缺陷检测方法.
- 为了提高多层次缺陷的准确性,稳定性和检测速度.
主要方法:
- 在部模块中实现了轻量级的GsConv卷积,以降低计算成本.
- 在骨干中集成SENetV2注意力机制,以改善特征表示.
- 使用自建的工业轴承环表面图像数据集进行培训和验证.
主要成果:
- 实现了97.8%的AP50准确度.
- 大,中,小缺陷的检测精度分别提高了2.4%,3.6%和2.3%.
- 达到每秒115 (FPS) 的检测速度.
结论:
- 与主流算法相比,改进的YOLOv8-GSS方法显示出更高的性能.
- 提议的改进有效地解决了工业轴承表面缺陷检测方面的挑战.
- 该方法在工业应用中,在准确性和检测速度方面都提供了显著的改进.
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