面向织物疵点检测的深度学习技术应用研究进展

doi:10.13475/j.fzxb.20240102302

纺织学报 ›› 2024, Vol. 45 ›› Issue (12): 234-242.doi: 10.13475/j.fzxb.20240102302

面向织物疵点检测的深度学习技术应用研究进展

刘燕萍¹, 郭佩瑶¹, 吴莹¹^,²^,³()

1.浙江理工大学服装学院, 浙江杭州 310018
2.浙江理工大学嵊州创新研究院, 浙江嵊州 312400
3.现代物流绿色低碳技术及产业化浙江省工程研究中心, 浙江温州 325000

收稿日期:2024-01-15 修回日期:2024-08-15 出版日期:2024-12-15 发布日期:2024-12-31
通讯作者: 吴莹(1988—),女,副教授,博士。主要研究方向图像处理技术、基于机器学习的疵点检测技术。E-mail:ying012688@zstu.edu.cn。
作者简介:刘燕萍(1999—),女,硕士生。主要研究方向为图像处理技术、基于深度学习的疵点检测技术。
基金资助:
浙江理工大学优秀研究生学位论文培育基金项目(LW-YP2024037);浙江省教育厅一般科研项目(Y202352846);浙江理工大学嵊州创新研究院科研项目(SYY2023B000003);浙江理工大学基本科研业务费青年创新专项项目(22076229-Y)

Research progress in deep learning technology for fabric defect detection

LIU Yanping¹, GUO Peiyao¹, WU Ying¹^,²^,³()

1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Sci-Tech University Shengzhou Innovation Research Institute, Shengzhou, Zhejiang 312400, China
3. Zhejiang Engineering Research Center for Green and Low Carbon Technology and Industrialization of Modern Logistics, Wenzhou, Zhejiang 325000, China

Received:2024-01-15 Revised:2024-08-15 Published:2024-12-15 Online:2024-12-31

摘要/Abstract

摘要：

为提高深度学习技术在疵点检测中的应用效率,推动纺织行业质量控制自动化与智能化发展。首先,对现有公开的疵点数据集进行整理,剖析织物疵点数据的现状及困境。其次,从监督学习、半监督学习和无监督学习三方面梳理了面向织物疵点检测的深度学习技术原理,对比各自的优缺点及适用场景。此外,对疵点检测领域常用的速度和精度评价指标进行了总结。最后,基于背景、检测方法及评价指标等多个维度,对深度学习各类网络在疵点检测任务中的实验结果进行了对比分析。结果表明,数据集质量是影响算法性能的关键因素。认为未来研究重点将是生成有织物纹理特性的高质量疵点,可自动标注的监督学习算法,以及提升无监督和半监督学习算法的检测性能。

关键词: 织物疵点检测, 深度学习, 目标检测, 疵点分类, 图像分割, 织物质量控制

Abstract:

Significance Automatic fabric defect detection is one of the key aspects of digital quality control in the textile industry. At present, the domestic fabric defect detection is mostly based on manual detection, but the traditional manual detection success rate of only 60%-75%, indicating that the method can't meet the demand for high-quality products. To overcome the drawbacks of manual defect detection, researchers have proposed a variety of learning-based defect detection algorithms. Compared with the manual detection, machine learning methods demonstrate a high detection rate, good stability and other characteristics. Bacause of the superiority of deep learning technology in defect detection, this technology is also used for fabric defect detection. In order to improve the efficiency of the application of deep learning technology in defect detection and to achieve digital quality control in the textile industry, the current status of research on deep learning technology in defect detection is discussed.

Progress Although traditional algorithms have achieved imroved results in some specific applications, there are still limitations when dealing with complex fabric textures. With the upgrading of computer hardware, the technology is superior in the fields of target detection and image classification, and is utilized in textile quality inspection. Since the introduction of deep learning, great breakthroughs have been made in target detection, which can be categorized into one-phase detection model and two-phase detection model in textile defect detection, both achieving better results in detection speed and detection accuracy. Due to the excellent feature extraction capability of neural networks, convolutional neural network (CNN) based classification networks are widely used for surface defect detection and classification, which can automatically learn different types of fabric defects and accurately categorize them into different classes. Various deep learning methods are superior to manual detection. Due to the difficulty in obtaining fabric datasets, research based on unsupervised learning and semi-supervised learning is gaining popularity, which trains on unlabeled data and a small amount of labeled data and reduces the dependence on labeled data. It can effectively deal with unlabeled datasets or situations where labeled data is scarce or unavailable, and it greatly reduces the working time compared to supervised learning where training is performed on labeled datasets.

Conclusion and Prospect This paper reviews the application of deep learning techniques to fabric defect detection. First, publicly available defect datasets are organized and analyzed. Secondly, the principles, advantages and disadvantages, and the scope of application of deep learning techniques for defect detection are summarized from three perspectives, i.e. supervised learning, semi-supervised learning and unsupervised learning. In addition, the commonly used speed and accuracy evaluation metrics in defect detection are sorted out. Finally, the experimental results of different deep learning networks in the detection task are objectively compared and analyzed, and the future development direction of fabric defect detection is envisioned. Supervised learning-based defect detection requires a large number of datasets, and the available public data resources are relatively scarce. Relying solely on manual labeling of fabric defects is not only time-consuming but also inefficient, therefore, automatic labeling of fabric defects and detection methods that do not require data labeling have become an important direction for future research. Currently, defect samples face many challenges in terms of data scarcity, labeling difficulty, and uneven data distribution, so unsupervised learning, weakly-supervised learning, zero-sample learning, and small-sample learning are receiving more and more attention in defect generation and detection. On the other hand, solving the data problem and developing defects with fabric texture characteristics is also one of the focuses of future research. Currently, most network structures are still designed manually. However, with the development of automatic machine learning techniques, more and more machines will be able to search and generate network architectures automatically, gradually replacing the traditional manual design.

Key words: fabric defect detection, deep learning, object detection, defect classification, image segmentation, quality control on fabric

中图分类号:

TS101.9

刘燕萍, 郭佩瑶, 吴莹. 面向织物疵点检测的深度学习技术应用研究进展[J]. 纺织学报, 2024, 45(12): 234-242.

LIU Yanping, GUO Peiyao, WU Ying. Research progress in deep learning technology for fabric defect detection[J]. Journal of Textile Research, 2024, 45(12): 234-242.

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链接本文: http://www.fzxb.org.cn/CN/10.13475/j.fzxb.20240102302

http://www.fzxb.org.cn/CN/Y2024/V45/I12/234

图/表 5

表1

图1

表2

疵点检测单双阶段目标检测模型分析"

检测算法	网络模型	改进	缺点	总结	文献
单阶段检测模型	YOLO-VOC	改变学习率来优化YOLO-VOC网络模型	网络训练时间相比于同类型网络更长	无需人工提取特征,可准确定位色织布疵点并进行分类	[8]
	YOLOv3	进行维度聚类,增加YOLO的检测层	检测速度慢,实时性还有待提高	该方法能够有效降低网络的错误率,检测精度提高	[9]
	S-YOLO V3	使用K均值聚类算法,修减卷积层得到S-YOLOV3模型	小目标检测易漏检	检测准确率达到94%速度达到5帧/s	[10]
	YOLOV4	主干模型用CSPDarkNet-53	复杂纹理背景检测精度低	精度为95.3%,检测速度为34帧/s	[11]
	YOLO-SCD	引入深度可分离卷积和注意力机制	多种类型织物疵点的检测不足	平均检测精度提高了8.49%,并大幅提升了检测速度	[12]
	SSD	改进SSD模型,利用较低的卷积特征层处理图像缺陷检测	泛化性较差,检测时效性还不足	在缺陷检索能力和检测精度上优于经典SSD目标检测模型	[13]
	SSD	添加全卷积挤压激励(FCSE)模块,调整默认框的数量	缺陷分割精度较低	实现高速检测,准确检测不同纹理织物表面的各种缺陷	[14]
	RetinaNet	添加高斯噪声的生成模型,添加分类模型来限制焦点生成器	小规模数据集检测,无法用于真实生产大规模数据	小规模织物疵点数据集上准确率达到83.4%	[15]
双阶段检测模型	Casecade R-CNN	使用多尺度训练,维度聚类方法,Soft-NMS代替NMS	织物图案相对简单,在复杂纹理疵点检测精度低	平均精度提高了8.9%	[16]
	Casecade R-CNN	Resnet50结合特征金字塔网络和可变形卷积,训练一系列IoUs逐步增加的探测器	检测速度提高,但还无法实现自动化检测	织物疵点检测达到了91.57%的准确率	[17]
	SPP-NET	NSCT图像分解、DBN提取特征、ROI与SPP网络结合	模型训练时间增加,检测速度慢	能够有效去除斑点噪声,提高高分辨率SAR变化检测的鲁棒性	[18]
	Faster R-CNN	ResNet50代替VGG16网络,ROI Align替换ROI pooling	只在简单纹理缺陷上进行测试,模型速度训练慢	检测精度和收敛能力均有较大提升	[19]
	Faster R-CNN	Gabor核嵌入Faster R-CNN	识别颜色变化区域时间将其误认为是污点缺陷	平均精度达到94.57%,而更快的R-CNN为78.98%	[20]
	Faster R-CNN	添加FPN特征金字塔、ROI对齐和Soft-NMS	检测框重叠不明显时Soft-NMS抑制效果差别较小	实现98%的检测精度,平均精度平均值85%	[21]

表2

图2

表3

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数据集名称	图像数量	疵点种类	图像分辨率/ 像素	织物种型	公开网址
TILDA数据库	3 200	8	768×512	8	https://lmb.informatik.unifreiburg.de/resources/datasets/tilda.en.html
阿里云天池织物疵点数据集	10 500	16	4 096×1 696	无	https://tianchi.aliyun.com/dataset/79336
香港大学纹理织物图像数据集FID	—	82	450×450	3	https://ytngan.wordpress.com/codes/
AITEX	245	12	4 096×256	7	https://www.aitex.es/afid/
Kylberg纹理数据集	4 480	—	576×576	28	http://kylberg.org/kylberg-texture-dataset-v-1-0/
KTH-TIPS纹理图像数据集	810	—	1 280×960	10	https://www.csc.kth.se/cvap/databases/kth-tips/doc/kth_tips.html
DAGM2007	3 450	2	512×512	10	https://conferences.mpi-inf.mpg.de/dagm/2007/prizes.html

技术	数据集	网络	精度指标/%	速度指标	文献
检测网络	阿里云天池数据疵点数据库6 000张	Faster R-CNN	MAcc 94.6	—	[24]
	坯布疵点数据库2 000张	Faster R-CNN与Resnet结合	MAP 99.6	130 ms/张	[25]
	工厂自集6种疵点数据库810张	Faster R-CNN	MAcc 95.8	0.3 s/张	[20]
	TILDA数据库360张	Faster R-CNN	MAP 97	—	[26]
	碳纤维预浸染表面数据库1 000张	YOLOV2	检出率达到94	0.1 s/张	[27]
	阿里云天池疵点数据库5 096张	YOLO-SCD	MAP 82.9	46帧/s	[12]
	香港大学数据库(FID)	学生教师网络	MIoU 82.5	59帧/s	[45]
分割网络	DAGM 2007数据集2 099张图像	DeepLab v3+	AP 94.4,IoU 75.3	—	[51]
	自建数据集该数据集包含6类疵点	ClothNet	MIoU 78.8	—	[34]
	DeepFashion2数据集	Mask R-CNN	MIoU 75.9	—	[52]
	企业数据集样本4 360个,AITEX数据集245张	SCU Net	Acc 98.0	—	[53]
分类网络	TILDA数据库3 200张	ResNet512	MAcc 96.5	—	[54]
	TILDA数据库250张	其它CNN	Acc 97.8、R 97.6	—	[40]
	TILDA数据库3 200张	AE和VAE编解码网络	分类检出率达到90.0以上	—	[49]
	异性纤维光谱库3 200张	基于VGG改进的卷积神经网络	Acc 99.6	—	[39]
	蓝印花布纹样数据库21 212张	改进CifarNet网络	MAcc 99.6	—	[41]
	Fashion-MNIST 70 000张	Π model + ALC	Acc 82.0	—	[45]

面向织物疵点检测的深度学习技术应用研究进展

Research progress in deep learning technology for fabric defect detection

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