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基于轻量级YOLOv4网络的表面缺陷检测方法.

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  • 1College of Information Engineering, Dalian Ocean University, Dalian, 116021, China. lisongsong@dlou.edu.cn.

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这项研究介绍了M2-BL-YOLOv4,这是一种用于表面缺陷检测的轻量级深度学习模型. 改进的模型实现了高精度和实时应用的显著更快的检测速度.

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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:

  • 的自动表面缺陷检测对于质量控制至关重要.
  • 现有的深度学习模型往往受到大量参数数量和缓慢速度的影响,阻碍了实时应用.

研究的目的:

  • 开发一种轻量级和高效的深度学习模型,用于实时检测表面缺陷.
  • 改进YOLOv4算法,以提高检测表面缺陷的性能.

主要方法:

  • 修改了YOLOv4的CSPDarkNet53骨干,以一个倒置的残余结构来减少参数和增加速度.
  • 引入了一种新的BiFPN-Lite功能融合网络,以提高检测准确度.
  • 在表面缺陷数据集上评估了M2-BL-YOLOv4模型.

主要成果:

  • 在表面缺陷测试套件上达到93.5%的平均平均精度 (mAP).
  • 将模型参数减少到原始YOLOv4模型的60%.
  • 检测速度提高到每秒52.99 (FPS),提高了30%.

结论:

  • 拟议的M2-BL-YOLOv4模型为自动化表面缺陷检测提供了高效和准确的解决方案.
  • 轻量级的设计和改进的功能融合使实时检测能力成为可能.
  • 这一进步有助于在制造过程中加强质量控制.