De-DDPM: 可控、可迁移的缺陷图像生成方法

岳忠牧; 张喆; 吕武; 赵瑞祥; 马杰

doi:10.16383/j.aas.c230688

De-DDPM: 可控、可迁移的缺陷图像生成方法

doi: 10.16383/j.aas.c230688

岳忠牧^1,,
张喆^1,,
吕武^2,,
赵瑞祥^2,,
马杰^1,

1.
华中科技大学人工智能与自动化学院武汉 430074
2.
中国船舶集团有限公司航海科技有限责任公司北京 100070

基金项目: 国家自然科学基金(U1913602, 61991412), 装备预先研究基金(50911020603)资助.

详细信息

作者简介:
岳忠牧：华中科技大学人工智能与自动化学院硕士研究生. 主要研究方向为缺陷数据生成, 表面缺陷检测. E-mail: HUST_Y2021@163.com

华中科技大学人工智能与自动化学院博士研究生. 主要研究方向为缺陷检测, 缺陷数据生成, 深度学习. 张喆与岳忠牧在本工作中同等贡献. E-mail: zhangzhe1997@hust.edu.cn

吕武：中国船舶集团有限公司航海科技有限责任公司高级工程师. 主要研究方向为综合导航, 装备智能维护. E-mail: 18911990785@163.com

赵瑞祥：中国船舶集团有限公司航海科技有限责任公司工程师. 主要研究方向为环境态势感知，船体缺陷检测. E-mail: zhaoruixiang12@126.com

马杰：华中科技大学人工智能与自动化学院教授. 主要研究方向为图像信息处理, 目标检测与识别, 无人艇环境感知. 本文通信作者. E-mail: majie@hust.edu.cn

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出版历程
- 收稿日期: 2023-11-07
- 录用日期: 2024-02-20
- 网络出版日期: 2024-06-30

De-DDPM: A Controllable and Transferable Defect Image Generation Method

YUE Zhong-Mu^1
,,
ZHANG Zhe^1
,,
LV Wu^2
,,
ZHAO Rui-Xiang^2
,,
MA Jie^1
,

1.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
2.
Marine Technology Co., Ltd., China State Shipbuilding Corporation Limited (CSSC), Beijing 100070

Funds: Supported by National Natural Science Foundation of China (U1913602, 61991412) and the Foundation of Equipment Pre-research Area (50911020603).

More Information

Author Bio:
YUE Zhong-Mu　Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers defect data generation and surface defect detection

ZHANG Zhe　Ph.D. candidate at the School of Artifical Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers defect detection, defect data generation, and deep learning. Zhang Zhe and Yue Zhong-Mu contributed equally to this work

LV Wu　Senior engineer at the Marine Technology Co., Ltd., China State Shipbuilding Corporation Limited (CSSC). His research interest covers integrated navigation and intelligent maintenance of equipment

ZHAO Rui-Xiang　Engineer at the Marine Technology Co., Ltd., China State Shipbuilding Corporation Limited (CSSC). His research interest covers environmental situational awareness and hull defect detection

MA Jie　Professor at the School of Artifical Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers image information processing, target detection and identification, and unmanned vessel environmental sensing. Corresponding author of this paper

摘要

摘要: 基于深度学习的表面缺陷检测技术是工业上的一项重要应用, 而缺陷图像数据集质量对缺陷检测性能有重要影响. 为解决实际工业生产过程中缺陷样本获取成本高、缺陷数据量少的痛点, 提出了一种基于去噪扩散概率模型(Denoising diffusion probabilistic model, DDPM)的缺陷生成方法. 该方法在训练过程中加强了模型对缺陷部位和无缺陷背景的差异化学习. 在生成过程中通过缺陷控制模块对生成缺陷的类别、形态、显著性等特征进行精准控制, 通过背景融合模块, 能将缺陷在不同的无缺陷背景上进行迁移, 大大降低新背景上缺陷样本的获取难度. 实验验证了该模型的缺陷控制和缺陷迁移能力, 其生成结果能有效扩充训练数据集, 提升下游缺陷检测任务的准确率.
- 数据增强 /
- 数据集扩充 /
- 缺陷图像生成 /
- 深度学习
Abstract: Surface defect detection technology based on deep learning is an important application in industry and the quality of defect image dataset has a significant impact on defect detection performance. A defect image generation method based on denoising diffusion probabilistic model (DDPM) is designed to address the pain points of high cost of obtaining defect samples and low amount of defect data in actual industrial production processes. This method enhances the model＇s differential learning of defect locations and defect free backgrounds during the training process. Through the defect control module during the generation process, this method accurately controls the category, morphology, saliency and other features of generated defects. Through the background fusion module, defects can be migrated on different defect free backgrounds, which greatly reducing the difficulty of obtaining defect samples on new backgrounds. The experiment has verified the defect control and defect migration capabilities of the model, and its generated results can effectively expand the training dataset and improve the accuracy of downstream defect detection tasks.
- Data augmentation /
- dataset expansion /
- defect image generation /
- deep learning

HTML全文

图 1 De-DDPM简要流程

Fig. 1 Brief process of De-DDPM

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图 2 DDPM简要流程

Fig. 2 Brief process of DDPM

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图 3 特征Unet网络结构

Fig. 3 The structure of feature Unet network

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图 4 De-DDPM单步生成过程结构

Fig. 4 The single-step generation process of De-DDPM

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图 5 缺陷控制模块结构

Fig. 5 The structure of defect control module

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图 10 缺陷控制效果

Fig. 10 Defect control effect

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图 6 背景融合模块结构

Fig. 6 The structure of background fusion module

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图 11 不同背景下缺陷迁移效果

Fig. 11 Defect migration effect in diverse backgrounds

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图 7 模型生成结果灰度分布统计

Fig. 7 Statistics of gray scale distribution

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图 8 数据集扩充实验流程

Fig. 8 The process of dataset augmentation experiment

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图 9 各模型生成结果

Fig. 9 The results of each model

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表 1 评价指标统计

Table 1 Statistics of evaluation metrics

评价指标	Pix2pix	StyleGAN2	DFMGAN	RePaint模型	所提方法
IS↑	1.388	1.368	1.301	1.474	1.541
FID↓	59.056	124.748	96.783	72.750	57.650
KID↓	0.024	0.098	0.053	0.044	0.020
MS-SSIM↓	0.189	0.161	0.174	0.187	0.159
PSNR↓	28.308	28.284	28.357	28.273	28.223
注: 1) RePaint模型使用裂纹掩码生成缺陷效果差, 改为区块掩码; 2) 箭头标明评价指标得分更好的方向.

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表 2 分类结果统计(%)

Table 2 Statistics of classification results (%)

测试集	Pix2pix		StyleGAN2		DFMGAN		RePaint模型		所提方法
测试集	缺陷检出率	总正确率	缺陷检出率	总正确率	缺陷检出率	总正确率	缺陷检出率	总正确率	缺陷检出率	总正确率
D1	30.77	65.38	26.92	63.46	7.69	53.85	13.46	54.81	88.46	94.23
D2	54.46	77.07	49.94	74.97	32.26	66.13	45.76	70.99	88.32	93.76
注: RePaint模型使用裂纹掩码生成缺陷效果差, 改为区块掩码.

下载: 导出CSV

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