对于钢板表面的实时缺陷检测算法:StarNet-GSConv-RetC3检测变压器 (SSR-DETR)
Zhuguo Zhou1, Yujun Lu1, Liye Lv1
1School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, China.
Annals of the New York Academy of Sciences
|May 13, 2025
概括
本研究引入了一种轻量级的SSR-DETR框架,用于工业钢表面缺陷检测,显著提高了准确性和效率. 这种新的方法增强了对小目标的识别,并优化了对现实应用的硬件部署.
科学领域:
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 工业钢板表面缺陷检测面临复杂模型,低效小目标识别和低效硬件部署的挑战.
- 现有的方法在实时工业应用中努力平衡检测精度和计算效率.
研究的目的:
- 提出轻量检测变压器 (SSR-DETR) 框架,以解决当前钢表面缺陷检测方法的局限性.
- 提高对小缺陷的识别能力,提高工业环境中的硬件部署效率.
主要方法:
- 开发了一个新的SSR-DETR框架,结合了StarNet骨干和恒星操作优化结构.
- 集成了RetBlock CSP瓶与3个卷积 (RetC3) 模块和分组空间卷积 (GSConv) 进行了改进的特征提取和多尺度聚合.
- 利用二维空间前置和明确的减弱机制来增强微观缺陷的几何特征提取.
主要成果:
- 在NEU-DET数据集上实现了88.2%的平均平均精度 (mAP) 和92.0%的分类精度,分别超过了4.8%和3.7%的基线.
- 与基线模型相比,计算负载减少了59.5%,模型大小减少了47.8%.
- 经过证明的高操作速度:PC上的98.1 FPS和RK3568嵌入式系统上的40.3 FPS (24.8 ms/frame).
结论:
- 拟议的SSR-DETR框架在轻质钢表面缺陷检测方面取得了重大进展,平衡了高精度与降低复杂度.
- 该框架符合工业场景的严格要求,能够有效和准确地检测钢板表面缺陷.
- 优化的架构和特征提取方法为实时缺陷检查系统提供了强大的解决方案.
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