基于改进的YOLOv9的钢表面缺陷检测方法
Cong Chen1, Hoileong Lee2, Ming Chen3
1School of Marine Information Engineering, Hainan Tropical Ocean University, Sanya, 572022, China.
Scientific reports
|July 11, 2025
概括
这项研究使用改进的YOLOv9算法增强了钢表面缺陷检测. 这种新的方法显著提高了识别小缺陷的准确性和效率,这对于智能制造质量控制至关重要.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算机视觉和人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 钢表面缺陷检测对于工业自动化质量控制至关重要.
- 不同的缺陷类型和大小,特别是小的缺陷,存在重大检测挑战,导致高错误率.
- 现有的方法与微妙的,小的缺陷作斗争,影响生产效率和产品质量.
研究的目的:
- 开发基于YOLOv9.9的改进钢表面缺陷检测算法.
- 为了提高小型缺陷的检测准确性和效率.
- 为了减少计算复杂性和提高多尺度目标检测能力.
主要方法:
- 实施深度可分离卷积 (DSConv) 来降低模型的复杂性.
- 集成了C3模块,用于有效的多层次特征融合和多尺度目标检测.
- 整合了双向特征金字塔网络 (BiFPN) 和DySample上采样,以改进小目标特征提取和本地化.
主要成果:
- 实现了78.2%的平均平均精度 (mAP),比基线增加1.8%.
- 与基线模型相比,达到82.5%的准确性,比起基线模型有7.4%的改进.
- 减少了8.9%的模型参数数量,同时提高了性能.
结论:
- 改进的YOLOv9算法有效地解决了钢表面缺陷检测方面的挑战,特别是对于小型目标.
- 拟议的改进导致检测准确性,本地化和计算效率的显著改善.
- 这项研究为推进智能制造和钢铁生产的质量控制提供了实际价值.
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