SH-DETR:通过改进的变压器架构来提高钢表面缺陷检测和分类
Shouluan Wu1, Hui Yang1, Liefa Liao1,2
1Jiangxi University of Science and Technology, Nanchang, Jiangxi, China.
PloS one
|November 11, 2025
概括
这项研究引入了用于检测钢表面缺陷的深度学习框架,通过使用多通道编码和多尺度特征融合来提高准确性和效率. 新的SH-DETR模型增强了计算机视觉应用中的缺陷识别.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 材料科学 材料科学 材料科学
背景情况:
- 钢表面缺陷检测至关重要,但由于缺陷的复杂性和多样性,具有挑战性.
- 传统模型在识别准确度低,分类能力不足等方面存在问题.
- 现有的方法需要先进的技术来准确识别缺陷.
研究的目的:
- 开发一个深度学习框架,用于增强钢表面缺陷检测.
- 解决传统模型在准确性和分类能力方面的局限性.
- 提高识别各种钢材表面缺陷的效率和有效性.
主要方法:
- 提出了一个新的深度学习框架,结合了多道随机编码和多尺度特征融合.
- 集成变压器架构的自我注意机制与卷积神经网络 (CNN) 使用ResNet18.
- 引入了一种多通道混合编码模块和一个上方样本连接的简单无参数注意模块 (UPC-SimAM) 用于特征提取和融合.
主要成果:
- 与最先进的方法相比,SH-DETR模型在NEU-DET和GC10-DE数据集上表现出卓越的性能.
- 在NEU-DET数据集上实现了91.72%的分类准确度,83.03%的mAP@0.5和45.55%的mAP@0.5:0.95.
- 在GC10-DE数据集上获得了76.73%的分类精度,65.03%的mAP@0.5和32.46%的mAP@0.5:0.95.
结论:
- 拟议的SH-DETR模型显著提高了钢表面缺陷检测的效率和准确性.
- 废弃研究和可视化证实了该模型的有效性和潜力.
- 该框架为计算机视觉中的复杂缺陷识别提供了一个有希望的解决方案.
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