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一种基于改进的RetinaNet的钢表面缺陷检测方法.

Zhanglin Yang1, Yu Liu2

  • 1College of Mechanical and Automotive Engineering, ChuZhou Polytechnic, Chuzhou, 239000, China.

Scientific reports
|February 19, 2025
PubMed
概括

这项研究引入了改进的RetinaNet用于钢表面缺陷检测,通过调整特征提取和化来提高准确性. 新方法显著提高了检测性能,并减少了实际应用的模型参数.

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:

  • 由于不同类型,形状和背景相似性,钢表面缺陷对检测具有挑战.
  • 由于这些复杂的视觉特征,现有的方法往往难以准确.

研究的目的:

  • 开发一种先进的钢质表面缺陷检测方法,以更高的准确性和效率.
  • 为了提高特征提取和融合对各种缺陷外观的适应性.

主要方法:

  • 将可变形卷曲集成到ResNet骨干中,以进行自适应性特征提取.
  • 实施一个CA-BiFPN,用于增强特征融合的注意力机制.
  • 引入IA-BCELoss函数用于对准确检测盒的配对分类和回归.

主要成果:

  • 拟议的方法实现了比原始RetinaNet的平均平均精度 (mAP) 提高6%,达到81.5%.
  • 与YOLOv7-X和YOLOX-L相比,它表现出更高的性能,mAP增加分别为5.2%和5.3%.
  • 该模型还实现了显著的参数减少,与YOLOv7-X和YOLOX-L相比减少了37.96M和21.23M.

结论:

关键词:
缺陷检测 检测缺陷检测 检测缺陷检测可变形卷积的可变形卷积.网膜网 (RetinaNet) 是一个网膜网.钢表面缺陷 钢表面缺陷

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  • 改进的RetinaNet方法为钢表面缺陷检测提供了卓越的性能.
  • 适应性特征提取,增强的融合和合损失功能有助于提高准确性和实用价值.