FasterNet-YOLO用于实时检测钢表面缺陷算法
Shiwei Yu1, Zelin Liu1, Liang Zhang1
1CGN Digital Technology Co., Ltd., Shanghai, China.
PloS one
|May 9, 2025
概括
本研究引入了一种增强的FasterNet-YOLO模型,用于检测钢表面缺陷. 改进的模型实现了更高的精度和更快的实时检测速度,有利于工业应用.
科学领域:
- 工业自动化 工业自动化
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 钢表面缺陷检测对于制造中的质量控制至关重要.
- 在工业物体检测中平衡精度和实时性能仍然是一个挑战.
研究的目的:
- 开发一个改进的FasterNet-YOLO模型,以高效准确地检测钢表面的缺陷.
- 为了提高工业应用的实时检测能力.
主要方法:
- 使用FasterNet重建YOLOv5骨干网络以实现模型轻量化.
- 改进了YOLOv5子网络,具有深度可分离的卷积,并将Swin变压器集成到C3模块中.
- 利用BiFPN进行有效的特征融合,以改善各种尺度的检测.
主要成果:
- 模型参数减少了49.4%,GFLOPs减少了57.0%.
- 平均精度 (mAP) 提高了6.2%,每秒 (FPS) 提高了54.1%.
- 与原始模型相比,在检测准确度和速度方面都取得了显著的改进.
结论:
- 拟议的FasterNet-YOLO模型有效地解决了实时钢表面缺陷检测的挑战.
- 整合FasterNet,深度可分离卷积,Swin变压器和BiFPN,可以实现更高效,更准确的检测系统.
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