基于轻量级YOLOv4网络的表面缺陷检测方法
Songsong Li1, Shangrong Guo2, Zhaolong Han2
1College of Information Engineering, Dalian Ocean University, Dalian, 116021, China. lisongsong@dlou.edu.cn.
Scientific reports
|July 8, 2023
概括
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.
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.
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