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一个基于YOLOv11的轻量级框架用于小钢缺陷检测,具有一个新增的功能融合模块
Yongyao Wang1, Haiyang Sun2, Kai Luo1
1College of Electrical and Information Engineering, Beihua University, JiLin, 132000, JiLin, China.
Scientific reports
|October 2, 2025
概括
本研究介绍了PSF-YOLO,一种轻型钢质缺陷检测模型. 它在计算能力有限的设备上提高了小物体检测效率,将模型参数减少了25%.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 用于缺陷检测的深度学习模型在资源有限的终端设备上部署时面临挑战.
- 现有的模型通常在检测小缺陷方面表现出低效率.
研究的目的:
- 提出一种轻量级的钢质表面缺陷检测模型,即基于金字塔的小目标融合YOLO (PSF-YOLO),以提高终端设备的性能.
- 增强小物体检测能力,减少计算资源需求.
主要方法:
- 开发了基于改进的YOLOv11n物体检测框架的PSF-YOLO.
- 使用幽灵卷积 (GhostConv) 来减少参数和多维融合子 (MDF-Neck) 来增强小物体感知.
- 包含虚拟融合头和注意力合模块,用于多维集成和改进特征提取.
主要成果:
- 与基线模型相比,PSF-YOLO将模型参数减少了25%.
- 在 GC10-DET+ 数据集中,在 [公式:见文本] 实现了 3.2% 的改进,在 [公式:见文本] 实现了 3.3% 的改进.
- 在总体检测性能方面显著提高.
结论:
- 在有限的计算能力的终端设备上,PSF-YOLO为钢表面缺陷检测提供了实用和高效的解决方案.
- 拟议的模型为在资源有限的环境中部署深度学习模型提供了有价值的见解.
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