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ZFD-Net:基于改进的YOLOV5的钢表面花缺陷检测模型

Yang Gao¹, Hanquan Zhang², Lifu Zhu³

¹State Key Laboratory of Digital Steel, Northeastern University, Shenyang, China.

|June 13, 2025

概括

这项研究介绍了ZFD-Net,这是一种新型的深度学习模型,用于实时准确检测板上的花缺陷. 该模型增强了特征提取和融合,在新数据集上表现优于现有方法.

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
工业自动化工业自动化

背景情况:

由于复杂的工业环境,区分花缺陷与板背景具有挑战性.
高生产线速度需要实时缺陷检测方法,以提高准确性和速度.
现有的检测技术很难满足对实时,准确的花缺陷识别的需求.

研究的目的:

开发一个先进的深度学习模型,ZFD-Net,用于高效和准确的花在板上的缺陷检测.
为实时工业应用解决当前方法在速度和准确性方面的局限性.
创建一个全面的数据集用于花缺陷检测,以促进未来的研究.

主要方法:

基于改进的YOLOv5架构提出的ZFD-Net模型.
集成了一个新的跨阶段部分变压器 (CSTR) 模块,用于增强全局特征提取.
采用双向特征金字塔网络 (Bi-FPN) 进行多尺度缺陷细节融合.
引入了一个Cross ResNet SIMAM FasterNet (CRSFN) 模块,以优化推理速度和检测准确度.
构建了一个高质量的,公开不可用的数据集,用于花缺陷检测.

主要成果:

与最先进的方法相比,ZFD-Net在自建数据集上表现出更高的性能.
该CSTR模块改进了接收场和全球特征提取能力.
双FPN有效地在不同尺度上融合了缺陷细节.
CRSFN模块提高了检测速度,同时保持了高精度.
新创建的数据集为花缺陷研究提供了宝贵的资源.

结论:

采用ZFD-Net,为化板材实时检测花缺陷提供了显著的进步.
拟议的模型架构和模块有效地解决了工业环境中准确性和速度的挑战.
开发专门的数据集克服了该领域的一个关键局限性,使得进一步的进展成为可能.

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