DES-YOLO:一种用于实时检测造表面缺陷的新型模型
Chengjun Wang1, Jiaqi Hu2,3, Chaoyu Yang1
1School of Artificial Intelligence, Anhui University of Science and Technology, Huainan, Anhui, China.
PeerJ. Computer science
|September 24, 2024
概括
一种名为DES-YOLO的新方法,可以准确地实时检测件中的小型和类似的表面缺陷. 这一进步通过克服传统检查技术的局限性,提高了造品质的控制.
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
- 制造业 工程 制造工程
背景情况:
- 造质量控制中的传统表面缺陷检查方法面临着高精度检测轻微缺陷和具有相似特征的缺陷的挑战.
- 现有的技术经常因其微妙的差异而与收缩孔和渣渣孔等缺陷作斗争,导致精度降低.
研究的目的:
- 推出 DES-YOLO,一种新的实时方法,用于更好地检测件的表面缺陷.
- 提高在造检查中识别小规模和类似特征缺陷的准确性和速度.
主要方法:
- DES-YOLO模型集成了DSC-Darknet骨干和全球注意力机制 (GAM),以改善特征识别.
- 使用增强的金字塔聚合模块,以更好地表示小缺陷的特征.
- 整合了Slim-Neck架构和SIoU界限框回归损失,以实现高效的实时处理.
主要成果:
- 在CSD-DET数据集上,DES-YOLO模型实现了92.6%的平均平均精度 (mAP).
- 该方法显示单图像推断速度为3.9毫秒,可实现实时检测.
- 在检测具有高度相似性和小尺度的缺陷方面有显著的改进.
结论:
- DES-YOLO提供了一种强大而高效的解决方案,用于实时检测件的表面缺陷.
- 拟议的方法有效地解决了传统检查技术的局限性,提高了造品质控制.
- 这一进步有助于在生产环境中快速准确地识别造表面缺陷.
相关概念视频
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