使用YOLOv9优化表面缺陷检测:先进的骨干模型的作用
Zhonglin Zeng1,2, Hongyang Wang2, Chi Yao2
1D'Amore-McKim School of Business, Northeastern University, Boston, MA, United States.
Frontiers in artificial intelligence
|October 27, 2025
概括
这项研究优化了使用YOLOv9与各种脊柱的钢带表面缺陷检测. RepViT实现了最高的准确性,而GhostNet为工业应用提供了最佳的效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 像YOLO这样的物体检测模型对于识别表面缺陷至关重要.
- 优化这些模型对于有效的工业检查至关重要.
研究的目的:
- 评估六个不同的骨干网络集成到钢带表面缺陷检测的YOLOv9框架中.
- 在准确性,计算复杂性和效率方面比较它们的性能.
主要方法:
- 在YOLOv9框架中集成ResNet50,GhostNet,MobileNetV4,FasterNet,StarNet和RepViT的骨干.
- 对NEU-DET和GC10-DET数据集进行了系统评估.
- 使用mAP50,F1-score,参数数量和GFLOPs等指标比较的骨干.
主要成果:
- RepViT表现出优越的整体性能,mAP50为68.8%,精度回忆平衡.
- 幽灵网提供了优异的计算效率,具有41.2M参数和190.2 GFLOPs.
- 使用YOLOv5-m验证了结果,证实了一致性.
结论:
- 推RepViT用于高精度的表面缺陷检测.
- 像GhostNet这样的轻量级架构适合实时工业应用.
- 该研究为在缺陷检测任务中选择骨干提供了实际指导.
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