中文版 | English
题名

面向转产过程的外观异常检测方法研究

其他题名
RESEARCH ON APPEARANCE ANOMALY DETECTION METHOD ORIENTED TO MANUFACTURING CHANGEOVER PROCESS
姓名
姓名拼音
Liu Jiaqi
学号
12132346
学位类型
硕士
学位专业
0809 电子科学与技术
学科门类/专业学位类别
08 工学
导师
郑锋
导师单位
计算机科学与工程系
论文答辩日期
2024-05-12
论文提交日期
2024-06-24
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

      工业外观异常检测对于推动生产力发展有着关键作用,同时作为计算机视觉落地的热门方向引起了众多研究人员的关注。异常的不可预测性导致难以遍历所有类型的异常样本,因而与目标检测和语义分割研究不同,异常检测主要在仅有正常样本参考的无监督场景下展开。然而,当下大多研究着眼于有充足数据训练的检测场景,而忽略了实际工业生产中会遇到的一些特殊场景。“转产”过程,又名“换产”、“换线”,就是当前被大多研究忽略的特殊场景之一。生产线投产早期,转产到未批量生产过的产品时参考数据较少,导致转产过程的冷启动阶段面临数据匮乏的问题。产线趋于成熟之后,持续转产阶段在不同产品间切换会导致模型重复部署、额外消耗时间。为了解决这两个问题,本文从以下两个方面分别展开了研究:
      (1)冷启动阶段所面临的主要问题是产线启动前的数据匮乏问题,本文从如何利用产品原型的角度出发,模拟真实场景构建了 3D 点云异常检测数据集Real3D-AD,并提出了以点云原型配准和双流记忆库为基础的异常检测方法Reg3D-AD,在 Real3D-AD 数据集上取得了最领先的性能,从而较好地模拟并解决了冷启动阶段的异常检测问题。
      (2)持续转产阶段所面临的主要问题是生产线不同产品快速切换,导致模型需要重复训练部署。本文将其抽象为了持续学习场景下的异常检测问题,全面调研了现有异常检测方法在持续学习场景下的表现,并设计了基于提示查询的无监督持续异常检测框架来应对持续学习场景下的异常检测问题。在MVTec AD数据集和VisA数据集上,本文所设计的框架在持续学习场景下超过了现有最先进的异常检测方法,为持续转产阶段的异常检测问题提供了可靠的解决方案。
      本文从工业生产线在转产过程中在冷启动阶段和持续转产阶段面临的问题出发展开研究。针对冷启动阶段所面临数据匮乏问题构建了对应的点云模拟数据集和可靠的点云异常检测基线方法,针对持续转产阶段面临的模型重复部署问题设计了通用的无监督持续异常检测框架,为转产过程的两种特殊阶段的外观异常检测提供了可靠的解决方案。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2024-06
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刘佳奇. 面向转产过程的外观异常检测方法研究[D]. 深圳. 南方科技大学,2024.
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