斯温变压器 -YOLOv5用于轻质热钢带表面缺陷检测算法
Qiuyan Wang1, Haibing Dong1, Haoyue Huang2
1School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.
PloS one
|January 25, 2024
概括
本研究介绍了一种改进的Swin-Transformer-YOLOv5模型,用于检测热钢带的表面缺陷. 增强型号实现了更高的准确性和效率,同时轻量化,使其适合工业应用.
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:
- 热钢带的自动视觉检查对于工业质量控制至关重要.
- 现有的缺陷检测算法面临着准确性,速度和适用于低功耗平台的挑战.
研究的目的:
- 为热钢带开发一种轻量级且准确的表面缺陷检测算法.
- 提高故障识别系统的实时性能和效率.
主要方法:
- 提出了一个改进的Swin-Transformer-YOLOv5模型,一个单阶段物体检测框架.
- 集成的GhostNet用于模型轻量化,Swin-Transformer在C3模块内处理复杂的背景和类别,CoordAttention用于增强的特征提取,BiFPN用于多尺度的特征融合.
主要成果:
- 改进后的模型显著优于标准目标检测算法.
- 与原始模型相比,实现了8.39%的mAP (平均平均精度) 增加.
- 模型参数减少了36.6%,GFLOPs减少了40.0%,重量减少了34.7%.
结论:
- 开发的Swin-Transformer-YOLOv5型号在热钢带表面缺陷检测方面提供了卓越的性能.
- 该模型的效率和减少的计算要求使其非常适合在低算法平台上部署.
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