TaDP-Det:用于工业表面缺陷检测的半监督纹理感知动态伪标签探测器
Qiwu Luo1, Weiyu Zhan1, Jiaojiao Su1
1School of Automation, Central South University, Changsha 430006, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了TaDP-Det,这是一种用于工业表面缺陷检测的新型半监督物体检测方法. 它使用纹理增强模块和类智能动态过来提高伪标签质量,提高未标签数据的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业质量控制 工业质量控制
背景情况:
- 表面缺陷检测对于工业质量控制至关重要.
- 获取标记数据以检测缺陷是昂贵且耗时的.
- 半监控对象检测 (SSOD) 使用未标记的数据来降低标签成本,但面临着工业图像挑战,如模两可的纹理和类不平衡.
研究的目的:
- 为工业表面缺陷检测开发一种有效的半监督物体检测方法.
- 提高从未标记的工业图像中生成的伪标签的质量.
- 为了应对前景背景混和类依赖的检测困难等挑战.
主要方法:
- 提出了TaDP-Det,这是一个半监督的探测器,具有伪标签质量的双重增强.
- 引入了一个纹理增强模块 (TEM) 来放大浅层骨干阶段的纹理线索.
- 采用适应性值的高斯混合模型实施了基于类的动态伪标签过 (CDPF) 方案.
主要成果:
- 在NEU-DET,GC10-DET和PCB-DEFECT数据集上,TaDP-Det在最先进的SSOD基线上表现优越.
- 在缺陷检测的平均平均精度 (mAP) 中取得了持续的改进.
- 展示了有效性,只有适度的计算开销.
结论:
- 通过改进的特征表示和自适应过,TaDP-Det显著提高了伪标签质量.
- 该方法在工业应用中有效地进行可靠的半监督缺陷检测.
- 在质量控制中,TaDP-Det提供了一个切实可行的解决方案,以减少对专家注释的依赖.
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