用高效的变压器语义补充进行缺陷细分的增量学习
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IEEE transactions on neural networks and learning systems
|September 9, 2025
概括
本研究引入了一种用于工业表面缺陷细分的新方法,该方法可以提高新缺陷类型的准确性. 它使用基于变压器的模块和蒸技术来克服现有模型的局限性,并防止数据被遗忘.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 工业表面缺陷的语义细分对于质量控制至关重要.
- 现有的模型在增量学习过程中与新的缺陷类型和灾难性遗忘作斗争.
- 缺陷和背景之间的低对比度使细分复杂化.
研究的目的:
- 开发一个有效的增量学习方法,用于工业表面缺陷细分.
- 解决现有模型在处理新缺陷类和低对比场景方面的局限性.
- 提高缺陷细分系统的适应性和性能.
主要方法:
- 引入了一个基于变压器的插件和播放的语义补充模块 (TSCM),将全球背景集成到CNN中.
- 拟议的多尺度空间聚合蒸 (MSPD) 在增量更新期间保持空间关系.
- 实施了适应性重量融合 (AWF) 策略,以平衡模型稳定性和可塑性.
主要成果:
- 拟议的方法在增量细分场景中显著优于现有的方法.
- TSCM有效地融合了全球和本地信息,提高了细分的准确性.
- MSPD和AWF成功地减轻了灾难性遗忘,并改善了特征对齐.
结论:
- 开发的方法为不断变化的工业缺陷细分任务提供了强大的解决方案.
- TSCM,MSPD和AWF的组合提供了卓越的性能和适应性.
- 这项工作提升了制造业自动化质量检查的能力.
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