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Updated: May 6, 2026

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工业聚合物薄膜的增强YOLOv8:为微米级缺陷检测提供半监督框架
Xiaoxia Yu1, Bingyu Hu2,3, Weifeng Jiang4
1Zhejiang Juhua Co Ltd., Quzhou, China.
Frontiers in artificial intelligence
|September 25, 2025
概括
本研究介绍了一种增强的YOLOv8模型,用于检测聚合物薄膜上的微缺陷. 改进的方法显著提高了小目标的准确性,满足了工业质量控制需求.
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:
- 通过挤压生产聚合物薄膜可以引入影响产品质量的微缺陷.
- 传统的机器学习方法用于缺陷检测缺乏准确性和适应性.
研究的目的:
- 提出一个改进的YOLOv8模型,用于准确检测聚合物薄膜上的微缺陷.
- 增强小目标的检测和提高概括能力.
主要方法:
- 实施了一种改进的YOLOv8模型,结合了CBAM注意力机制.
- 使用了半监督学习与平均教师方法和有限的标记数据.
- 采用了基于规范化瓦斯斯坦距离 (NWD) 和多门口罩细分算法的改进损失函数.
主要成果:
- 与基线YOLOv8模型相比,mAP@0.5的增加达到了8.26%.
- 对于小型目标检测,其精度非常高,在各种图像大小中检测率超过95.0%.
- 与传统方法相比,在各种照明和污染条件下表现出优越的性能.
结论:
- 改进的YOLOv8算法满足了对高精度的聚合物薄膜中小目标缺陷检测的严格工业要求.
- 未来的研究将专注于进一步提高缺陷检测模型的准确性和稳定性.
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