基于改进的YOLOv8s的液体容器接缺陷检测.
Zonghang Li1, Tao Song1, Bin Zhou1
1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种增强的YOLOv8s算法,用于检测汽车液体储中的接缺陷. 改进的模型显著提高了各种类型的缺陷的检测精度,提高了制造中的质量控制.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 汽车液体储上的接接缺陷给质量控制带来了挑战.
- 传统的检测方法对于各种缺陷形状和尺寸是不够的.
研究的目的:
- 开发一种先进的算法,以准确检测液体储存器接缺陷.
- 为了提高YOLOv8s模型的性能,用于检测尺寸变化和复杂特征的缺陷.
主要方法:
- 提出了一种优化的YOLOv8s算法,该算法包括修复参数化通用特征金字塔网络 (RepGFPN) 和一个小物体检测头.
- 取代了空间金字塔聚合快速 (SPPF) 用焦点调制网络 (FocalNets) 来进行复杂的缺陷识别.
- 集成级联群注意力 (CGA) 机制,以减少冗余特征信息传播.
主要成果:
- 与原来的YOLOv8s模型相比,实现了mAP@0.5的6.3%和mAP@0.5:0.95的4.3%的改进.
- 在火山口 (3.9%),孔隙 (13.5%),下切 (5.0%) 和缺乏聚变 (2.5%) 中显示出显著的AP改进.
- 在液体储和钢管接缺陷数据集上,超越了最先进的模型.
结论:
- 增强的YOLOv8s算法为检测汽车部件中各种接缺陷提供了卓越的性能.
- 建议的优化有效地解决了与缺陷大小变化和复杂性相关的挑战.
- 这种方法为制造业的自动化质量检查提供了强大的解决方案.
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