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

Abstract

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

CLC Number:

TS101.9

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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URL: http://www.fzxb.org.cn/EN/10.13475/j.fzxb.20240102302

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Figures/Tables 5

Tab.1

Fig.1

Tab.2

Comparison and analysis of one and two stage object detection models"

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]

Tab.2

Fig.2

Tab.1

References 54

[1]	CHAN C H, PANG G K H. Fabric defect detection by fourier analysis[J]. IEEE Transactions on Industry Applications, 2000, 36(5): 1267-1276.
[2]	DI L, LONG H B, LIANG J Z. Fabric defect detection based on illumination correction and visual salient features[J]. Sensors(Basel), 2020. DOI: 10.3390/s20185147.
[3]	BUMRUNGKUN P. Defect detection in textile fabrics with snake active contour and support vector ma-chines[C]// 11th International Conference on Computer and Electrical Engineering (ICCEE).Tokyo: IOP Publishing Ltd, 2018(1195): 1742-6596.
[4]	WU Y, ZHOU J, AKANKWASA N T, et al. Fabric texture representation using the stable learned discrete cosine transform dictionary[J]. Textile Research Journal, 2019, 89(3): 294-310.
[5]	ZOU Z X, CHEN K Y, SHI Z W, et al. Object detection in 20 years: a survey[J]. Proceedings of the IEEE, 2023, 111(3): 257-276.
[6]	王斌, 李敏, 雷承霖, 等. 基于深度学习的织物疵点检测研究进展[J]. 纺织学报, 2023, 44(1): 219-227.
	WANG Bin, LI Min, LEI Chenglin, et al. Advances in deep learning-based fabric defect detection[J]. Journal of Textile Research, 2023, 44(1): 219-227.
[7]	程旭, 宋晨, 史金钢, 等. 基于深度学习的通用目标检测研究综述[J]. 电子学报, 2021, 49(7): 1428-1438.
	CHENG Xu, SONG Chen, SHI Jingang, et al. A review of deep learning-based generalized target detection[J]. Acta Automation Sinica, 2021, 49(7): 1428-1438.
[8]	ZHANG H W. Yarn-dyed fabric defect detection with yolov2 based on deep convolution neural net-works[C]// ZHANG L J, LI P F, GU D. IEEE 7th Data Driven Control and Learning Systems Confe-rence (DDCLS). Enshi: IEEE, 2018: 170-174.
[9]	JING J F, ZHUO D, ZHANG H H, et al. Fabric defect detection using the improved yolov3 model[J]. Journal of Engineered Fibers and Fabrics, 2020. DOI: 10.1177/1558925020908268.
[10]	ZHOU J, JING J F, ZHANG H H, et al. Real-time fabric defect detection algorithm based on s-yolov3 model[J]. Laser & Optoelectronics Progress, 2020. DOI: 10.3788/LOP57.161001.
[11]	DLAMINI S, KAO C Y, SU S L, et al. Development of a real-time machine vision system for functional textile fabric defect detection using a deep yolov4 model[J]. Textile Research Journal, 2022, 92(5/6): 675-690.
[12]	LUO X, NI Q, TAO R, et al. A light weight detector based on attention mechanism for fabric defect detec-tion[J]. IEEE Access, 2023(11): 33554-33569.
[13]	LIU Z F. Fabric defects detection based on ssd[C]// LIU S L, LI C L, DING S, et al. 2nd International Conference on Graphics and Signal Proceed-sing (ICGSP).Sydney: [s.n.]2018: 74-78.
[14]	XIE H S, ZHANG Y F, WU Z S. An improved fabric defect detection method based on ssd[J]. Aatcc Journal of Research, 2021(8): 182-191.
[15]	QIN Y J. Focus generator with score classification on fabric defect detection[C]// CHEN M, QI L, SUN Y.31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI).Portland, OR: IEEE, 2019: 1708-1714.
[16]	LI F. Bag of tricks for fabric defect detection based on cascade R-CNN[J]. Textile Research Journal, 2021, 91(5/6): 599-612.
[17]	LI H H. Integrating deformable convolution and pyramid network in cascade R-CNN for fabric defect detec-tion[C]// ZHANG H, LIU L, ZHONG H, et al. IEEE International Conference on Systems, Man, and Cybernetics (SMC).Toronto: IEEE, 2020: 3029-3036.
[18]	HASHIMOTO Y, WATANABE Y, TAKANO H, et al. High diagnostic yield using advanced artificial intelligence in cytology of pancreatic cancer by eus-fna[J]. Gastroenterology, 2019, 156(6): S115-S115.
[19]	ZHAO J, ZHOU S, ZHENG Q, et al. Fabric defect detection based on transfer learning and improved faster R-CNN[J]. Journal of Engineered Fibers and Fabrics, 2022. DOI: 10.1177/15589250221086647.
[20]	CHEN M Q, YU L J, ZHI C, et al. Improved faster R-CNN for fabric defect detection based on gabor filter with genetic algorithm optimization[J]. Computers in Industry, 2022. DOI: 10.1016/j.compind.2021.103551.
[21]	HE D F, WEN J J, LAI Z H. Textile fabric defect detection based on improved faster R-CNN[J]. Aatcc Journal of Research, 2021, 8(SUPPL 1): 83-91.
[22]	KAHRAMAN Y, DURMUSOGLU A. Deep learning-based fabric defect detection: a review[J]. Textile Research Journal, 2023, 93(5/6): 1485-1503.
[23]	LIU Z F. Fabric defect detection based on faster R-CNN[C]// LIU X H, LI C L, LI B C, et al. 9th International Conference on Graphic and Image Processing (ICGIP). Qingdao: ICGIP, 2017: 10615.
[24]	安萌, 郑飂默, 王诗宇, 等. 一种改进faster R-CNN的面料疵点检测方法[J]. 小型微型计算机系统, 2021, 42(5): 1029-1033.
	AN Meng, ZHENG Liaomo, WANG Shiyu, et al. An improved faster R-CNN method for fabric defect detection[J]. Journal of Chinese Computer Systems, 2021, 42(5): 1029-1033.
[25]	晏琳, 景军锋, 李鹏飞. Faster rcnn模型在坯布疵点检测中的应用[J]. 棉纺织技术, 2019, 47(2): 24-27.
	YAN Lin, JING Junfeng, LI Pengfei. Application of Faster RCNN model in blank fabric defect detec-tion[J]. Cotton Textile Technology, 2019, 47(2): 24-27.
[26]	WU J, LE J, XIAO Z T, et al. Automatic fabric defect detection using a wide-and-light network[J]. Applied Intelligence, 2021, 51(7): 4945-4961.
[27]	路浩, 陈原. 基于机器视觉的碳纤维预浸料表面缺陷检测方法[J]. 纺织学报, 2020, 41(4): 51-57.
	LU Hao, CHEN Yuan. Surface defect detection method of carbon fiber prepreg based on machine vision[J]. Journal of Textile Research, 2020, 41(4): 51-57.
[28]	张丽瑶, 王志鹏, 徐功平. 基于SSD的织物疵点检测的研究[J]. 电子设计工程, 2020, 28(6): 40-44.
	ZHANG Liyao, WANG Zhipeng, XU Gongping. Research on SSD-based fabric defect detection[J]. Electronic Design Engineering, 2020, 28(6): 40-44.
[29]	ZHAO H Q, ZHANG T S. Fabric surface defect detection using se-ssdnet[J]. Symmetry-Basel, 2022. DOI: 10.3390/sym14112373.
[30]	ZHANG Y. Steel defect detection based on modified retinanet[C]// GAO Y, SHEN L Y. 26th International Conference on Pattern Recognition/8th International Workshop on Image Mining-Theory and Applica-tions (IMTA). Montreal: IEEE, 2022: 3572-3579.
[31]	LIANG H, YANG J L, SHAO M W. Fe-retinanet: small target detection with parallel multi-scale feature enhancement[J]. Symmetry-Basel, 2021. DOI: 10.3390/sym13060950.
[32]	CHENG X, YU J B. Retinanet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021. DOI: 10.1109/TIM.2020.3040485.
[33]	LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2017, 39 (4): 640-651.
[34]	ZHANG T S, MA H R. Clothnet: sensitive semantic segmentation network for fabric defect detection[J]. Textile Research Journal, 2023, 93(1/2): 103-115.
[35]	马浩然, 张团善, 王峰, 等. 基于语义生成与语义分割的机织物疵点检测方法[J]. 轻工机械, 2023, 41(1): 66-73.
	MA Haoran, ZHANG Tuanshan, WANG Feng, et al. A defect detection method for woven fabrics based on semantic generation and semantic segmentation[J]. Light Industry Machinery, 2023, 41(1): 66-73.
[36]	ZHOU Z Y, YANG X F, JI J F, et al. Classifying fabric defects with evolving inception v3 by improved l2,1-norm regularized extreme learning machine[J]. Textile Research Journal, 2023, 93(3/4): 936-956.
[37]	SABEENIAN R S, PAUL E, PRAKASH C. Fabric defect detection and classification using modified vgg network[J]. Journal of The Textile Institute, 2023, 114(7): 1032-1040.
[38]	CELIK H I, DULGER L C, OZTAS B, et al. A novel industrial application of cnn approach: real time fabric inspection and defect classification on circular knitting machine[J]. Tekstil Ve Konfeksiyon, 2022, 32(4): 344-352.
[39]	李学良, 杜玉红, 任维佳, 等. 基于近红外光谱和残差神经网络的异性纤维分类识别[J]. 纺织学报, 2023, 44(5): 84-92.
	LI Xueliang, DU Yuhong, REN Weijia, et al. Classification and identification of anisotropic fibers based on near-infrared spectroscopy and residual neural network[J]. Journal of Textile Research, 2023, 44(5): 84-92.
[40]	ZHAO X Q, ZHANG M, ZHANG J J. Ensemble learning-based cnn for textile fabric defects classifica-tion[J]. International Journal of Clothing Science and Technology, 2021, 33(4): 664-678.
[41]	贾小军, 叶利华, 邓洪涛, 等. 基于卷积神经网络的蓝印花布纹样基元分类[J]. 纺织学报, 2020, 41(1): 110-117.
	JIA Xiaojun, YE Lihua, DENG Hongtao, et al. Convolutional neural network-based primitive classification of blue printed fabric pattern[J]. Journal of Textile Research, 2020, 41(1): 110-117.
[42]	LAINE S, AILA T. Temporal ensembling for semi-supervised learning[J]. arXiv, 2016. DOI: 10.48550/arXiv.1610.02242.
[43]	TARVAINEN A. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results[C]// VALPOLA H. 31st Annual Conference on Neural Information Processing Systems (NIPS). Long Beach, CA: Curran Associates Inc, 2017. DOI: 10.48550/arXiv.1703.01780.
[44]	MIYATO T, MAEDA S I, KOYAMA M, et al. Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(8): 1979-1993.
[45]	CHEN J M, YANG M, LING J. Attention-based label consistency for semi-supervised deep learning based image classification[J]. Neurocomputing, 2021(453): 731-741.
[46]	SHAO L H, ZHANG E H, MA Q R, et al. Pixel-wise semisupervised fabric defect detection method combined with multitask mean teacher[J]. IEEE Transactions on Instrumentation and Measurement, 2022. DOI: 10.1109/TIM.2022.3162286.
[47]	WEI C, LIANG J Z, LIU H, et al. Multi-stage unsupervised fabric defect detection based on dcgan[J]. Visual Computer, 2022, 39(12): 6655-6671.
[48]	HE X J, CHANG Z W, ZHANG L H, et al. A survey of defect detection applications based on generative adversarial networks[J]. IEEE Access, 2022(10): 113493-113512.
[49]	WEI W, DENG D X, ZENG L, et al. Real-time implementation of fabric defect detection based on variational automatic encoder with structure simi-larity[J]. Journal of Real-Time Image Processing, 2021, 18(3): 807-823.
[50]	OLIMOV B. Unsupervised deep learning-based end-to-end network for anomaly detection and localiza-tion[C]// SUBRAMANIAN B, KIM J.13th Interna-tional Conference on Ubiquitous and Future Networks (ICUFN). Barcelona: IEEE, 2022: 444-449.
[51]	TANG C W, FENG X X, WEN H T, et al. Semantic segmentation network for surface defect detection of automobile wheel hub fusing high-resolution feature and multi-scale feature[J]. Applied Sciences-Basel, 2021. DOI: 10.3390/app112210508.
[52]	顾梅花, 刘杰, 李立瑶, 等. 结合特征学习与注意力机制的服装图像分割[J]. 纺织学报, 2022, 43(11): 163-171.
	GU Meihua, LIU Jie, LI Liyao, et al. Combining feature learning and attention mechanism for garment image segmentation[J]. Journal of Textile Research, 2022, 43(11): 163-171.
[53]	CHENG L, YI J Z, CHEN A B, et al. Fabric defect detection based on separate convolutional unet[J]. Multimedia Tools and Applications, 2023, 82(2): 3101-3122.
[54]	JEYARAJ P R, NADAR E R S. Effective textile quality processing and an accurate inspection system using the advanced deep learning technique[J]. Textile Research Journal, 2020, 90(9/10): 971-980.

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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]

Research progress in deep learning technology for fabric defect detection

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