重新思考钢表面缺陷检测的多阶段校准融合网络和损失函数
Xue Li1, Shilong Zhao2, Yuting Wang3
1Computer teaching and research section, Shijiazhuang Medical College, Shijiazhuang, 050500, China.
Scientific reports
|November 27, 2025
概括
本研究介绍了RMCF-Net,这是一种用于检测钢表面缺陷的新型深度学习模型. 它提高了准确性和效率,这对于智能制造质量控制至关重要.
科学领域:
- 材料科学 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:
- 钢的表面缺陷大大损害了产品的可靠性和寿命.
- 由于不同大小,复杂的背景和微小的目标,检测这些缺陷具有挑战性,需要在准确性和效率之间保持平衡.
研究的目的:
- 开发一种先进的深度学习模型,用于准确高效地检测钢表面缺陷.
- 解决现有方法在处理各种缺陷特征和复杂工业环境方面的局限性.
主要方法:
- 提出了多阶段校准融合网络 (RMCF-Net),其中包括一个高效特征提取模块 (E-C2f) 和一个轻量级特征聚合模块 (LFAM).
- 引入了多阶段U形融合网络 (MUF-Net),具有适应性特征重权,以增强跨层特征交互.
- 开发了一个自适应调整函数 (QCIoU) 来改进边界框预测.
主要成果:
- 取得的最先进的平均精度 (mAP@0.5) 分数:在NEU-DET上为78.9%,在GC10-DET上为67.1%,在轨道组件上为71.9%.
- 保持了每秒119 (FPS) 的高检测速度.
结论:
- RMCF-Net在钢表面缺陷检测,平衡精度和效率方面表现出卓越的性能.
- 拟议的模型为智能制造中的质量控制提供了可靠的技术保证.
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