DSF-YOLO用于X射线图像中的接缺陷检测,具有动态分阶融合
Meng Zhang1, Yanzhu Hu2, Binbin Xu1
1School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, Beijing, 100876, China.
Scientific reports
|July 2, 2025
概括
DSF-YOLO通过使用新型功能融合和脱头来增强管道接缺陷检测. 这种框架提高了复杂缺陷的准确性和本地化,同时降低了计算负载,提供了重要的工业价值.
科学领域:
- 工业自动化和质量控制
- 计算机视觉用于非破坏性测试.
- 机器学习用于缺陷检测和检测.
背景情况:
- 接缺陷检测面临诸如模两可的界限和各种形状等挑战.
- 精确地定位缺陷对于工业应用至关重要.
- 现有的方法在复杂的缺陷特征和计算效率方面扎.
研究的目的:
- 提出DSF-YOLO,一个用于准确和高效的管道接缺陷检测的新框架.
- 为了应对模两可的边界,不同的形状和精确的定位所带来的挑战.
- 改进检测小型,模糊和复杂的接缺陷.
主要方法:
- 引入了动态分阶融合特征提取 (DSFFE) 以提高特征表示.
- 采用双重多尺度特征融合 (DMFF) 来聚合全球和本地特征.
- 使用基于SENetv2-ResNeXt的脱头,并进行多通道并行处理.
主要成果:
- 在X射线接缺陷数据集上,DSF-YOLO实现了74.7%的mAP50:95和99.1%的mAP50.
- 在检测准确度方面表现优于YOLOv8-X.
- 在FLOP中,计算复杂度降低了75%,在参数中降低了47.5%.
结论:
- DSF-YOLO是用于工业接缺陷检测的高效和准确的解决方案.
- 该框架有效地应对模糊的界限和小目标等挑战.
- 在工业环境中对质量控制具有显著的实际价值.
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