MCH-YOLOv12:研究基于改进的YOLOv12的型材表面缺陷检测算法
Yuyu Sun1, Heqi Yan1, Zongkai Shang1
1College of Computer Science and Technology, Changchun University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究介绍了MCH-YOLOv12,这是一种用于检测形状表面缺陷的先进算法. 它提高了小和不规则形状的缺陷的准确性,提高了工业检查质量.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在型材中检测表面缺陷对于工业生产中的质量控制至关重要.
- 现有的方法面临着不平衡的缺陷类别,小规模缺陷和不规则形状的挑战,限制了准确性和稳定性.
研究的目的:
- 为型材开发一个改进的缺陷检测算法 (MCH-YOLOv12).
- 为了增强特征提取和捕获细粒度缺陷特征.
- 提高适应各种缺陷大小和形状的适应性,以便实时检查.
主要方法:
- 修改了YOLOv12架构,将MultiScaleGhost卷积纳入骨干中,以增强功能表示.
- 在部引入了空间通道协作门线性单元 (SCCGLU),以捕捉方向和边缘特定的缺陷特征.
- 实施了混合头,结合了基于和无检测机制.
主要成果:
- MCH-YOLOv12在形状缺陷数据集上显示了更高的准确性和更少的类别失衡.
- 改进的算法显示出更好地检测小规模和不规则形状的缺陷.
- 与基线模型相比,实现了较低的计算复杂性 (参数和FLOP).
结论:
- MCH-YOLOv12为型材的表面缺陷检测提供了强大而准确的解决方案.
- 该算法的改进使其适用于实时工业检查应用.
- 该研究强调了MultiScaleGhost和SCCGLU模块在缺陷检测中的有效性.
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