纺织学报 ›› 2023, Vol. 44 ›› Issue (01): 219-227.doi: 10.13475/j.fzxb.20211105509

• 综合述评 • 上一篇    下一篇

基于深度学习的织物疵点检测研究进展

王斌1,2, 李敏2,3(), 雷承霖1,2, 何儒汉2,3   

  1. 1.武汉纺织大学 湖北省服装信息化工程技术研究中心, 湖北 武汉 430200
    2.武汉纺织大学 计算机与人工智能学院,湖北 武汉 430200
    3.武汉纺织大学 纺织服装智能化湖北省工程研究中心, 湖北 武汉 430200
  • 收稿日期:2021-11-10 修回日期:2022-10-04 出版日期:2023-01-15 发布日期:2023-02-16
  • 通讯作者: 李敏(1978—),女,副教授,博士。主要研究方向为图像处理与模式识别。E-mail:2008031@wtu.edu.cn。
  • 作者简介:王斌(1998—),男,硕士生。主要研究方向为计算机视觉。
  • 基金资助:
    中国高校产学研创新基金项目(2020HYA02015)

Research progress in fabric defect detection based on deep learning

WANG Bin1,2, LI Min2,3(), LEI Chenglin1,2, HE Ruhan2,3   

  1. 1. Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, Hubei 430200, China
    2. Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion, Wuhan Textile University, Wuhan, Hubei 430200, China
    3. School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei 430200, China
  • Received:2021-11-10 Revised:2022-10-04 Published:2023-01-15 Online:2023-02-16

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摘要：

为提高疵点检测的准确性和通用性,实现使用简洁而有效的形式对织物图像的特点和疵点的本质特征进行综合表达,首先,介绍了深度学习技术,对引入了深度学习的疵点检测方法进行综述,同时对深度学习与疵点检测的内在关系进行阐述;然后,分析总结了深度学习的概念及代表性的计算模型,并对引入深度学习的疵点检测方法进行归纳、总结和分类;最后,对典型的方法进行了分析,讨论了各种方法的优缺点,并对未来的研究趋势进行了展望。指出:随着深度学习的发展,探索更加通用的检测方法是推进深度学习在织物疵点检测领域应用的努力方向。

关键词: 深度学习, 疵点检测, 纺织品, 神经网络, 图像分割, 机器视觉

Abstract:

Significance With the development of science and technology, the improvement of product quality is highly demanded. Although the technologies used in producing textile products have undergone revolutionary advancement which contributes to the textile quality dramatically, defects in textile products such as fabrics remain to be a reality. Fabric defect detection plays an important role in textile industry, and fabric defect detection technology based on deep learning has been paid more and more attention. This paper reports a research and development progress in fabric defect detection based on deep learning.
Progress Deep learning is mainly composed of four steps, i.e., defining model and loss function, training the model, finding optimization method and loop iteration. The research focus for fabric defect detection method based on deep learning mainly includes deep learning models such as convolution neural network (CNN) and automatic encoder (AE). The stack denoising automatic encoder based on Fisher criterion introduces deep learning into this field for the first time, which provides a new idea for the application of deep learning to the field of defect detection. Convolution neural network has achieved good results in the field of image recognition because of its strong nonlinear fitting ability. More precision-based detection algorithms based on candidate regions and more speed-based algorithms based on regression analysis are present. While the advantages of convolution neural network is exploited, other methods are used for exploring the possibility of combined use of models, and provide new ways for defect detection.
Conclusion and Prospect Fabric defect detection methods based on deep learning in recent years are reviewed and summarized, and the effects of different models are compared in detail. Advantages, disadvantages and applicable scope of each model are analyzed, and future development of fabric defect detection method based on deep learning model is prospected. Deep learning models can improve the detection efficiency, but still have some deficiencies. In order to optimize the accuracy of fabric image defect detection, breakthrough should be made from the following aspects in the future. 1) High quality data sets should be established. 2) Specific evaluation criteria need to be established. 3) The applicability should be extended. A single detection method often has limitations, but when different defect detection methods are utilized to deal with different detection needs, the detection results are often different, therefore hybrid methods would have better applicability.

Key words: deep learning, defect detection, fabric, neural network, image segmentation, machine vision

中图分类号: 

  • TP181

图1

深度学习基本步骤"

表1

基于卷积神经网络模型的疵点检测方法"

序号 文献 关键技术 疵点召回 疵点分类 疵点位置回归
1 [18] VGG16 √ √ √
2 [25] CNN √ — —
3 [26] FPN √ √ √
4 [35] CNN √ — √
5 [36] PE TMCNN √ — √
6 [37] CNN √ √ √
7 [15] Faster RCNN, ResNet √ — √
8 [27] Faster RCNN √ √ √
9 [28] GAN √ √ √
10 [29] Faster RCNN, GAN √ — √
11 [30] DCGAN √ — —
12 [14] YOLOv3 √ √ √
13 [31] Faster RCNN √ — √
14 [32] RefinDet √ √ √
15 [33] DUW-CAM, L-SE √ — —
16 [34] CNN √ — √

表2

基于卷积神经网络的织物疵点检测方法性能"

序号 文献 数据集 精度 检测时间/(张·s-1) 疵点类型 硬件资源
1 [18] 自建数据集 亚麻布mAP:93.4%;花纹织物mAP:95.1% 亚麻布:21.5;花纹织物:21.1 破洞、污渍等5种 GTX 1060 GPU
2 [25] TILDA数据集 Acc:96.55% 19.6 油污、跳纱等6种 GTX 1280 GPU
3 [26] FBDF数据集 mAP:72.6% — 断裂、错纬等20种 TITAN RTX GPU
4 [35] TILDA数据集和自建数据集 TILDA数据集Acc:90.25%
自建数据集Acc:94.6%		 TILDA数据集:18.3
自建数据集:20.6		 纬缩、拆痕等7种 NVIDIA GTX 1080TI GPU
5 [36] Kylberg数据集和自建数据集 Kylberg数据集Acc:80.32%
自建数据集Acc:83.81%		 — 浆斑、油纱等14种 GTX 980Ti Nvidia GPU
6 [37] 自建数据集,Knitting数据集和Nano fiber数据集 自建数据集Acc:93.81%
Knitting数据集Acc:95.61%
Nano fiber数据集Acc:81.82%		 自建数据集:23.9
Knitting数据集:23.9
Nano fiber数据集:23.0		 脱纬、跳花等18种 GeForce RTX 2080 Ti
7 [15] 现场采集坯布 坯布mAP:94.71% 7.7 褶皱、线条等4种 NVIDIA GTX1080Ti
8 [27] 天池布匹数据集 Acc:94.67%, Recall:98.63% 4.3 污渍、断纬和破洞 Intel Core i5-82500U
9 [28] 自建数据集 mAP:94.8% — 棉结、污渍等8种 —
10 [29] 机器采集花纹织物 mAP:93.62% 5.4 带纱、破洞等6种 NVIDIA GeForce GTX 745
11 [30] TILDA数据集和自建数据集 TILDA数据集mAP:85.14%;自建数据集mAP:93.45% TILDA数据集:37;自建数据集:33 错纬、污渍等14种 NVIDIA Tesla P4
12 [14] 机器采集坯布和格纹布 坯布错误率:1.3%;格纹布错误率:1.02% 坯布:27.7;格纹布:21.8 破洞、油污等6种 GTX1080 Ti
13 [31] 机器采集斜纹和牛仔布 斜纹mAP:62.3%;
牛仔布mAP:92.5%		 斜纹:9.7;牛仔布:25.4 错纬、折痕等12种 RTX2080Ti GPU
14 [32] TILDA数据集,香港图案纹理数据集和DAGM200数据集 TILDA数据集mAP:80.2%;
香港图案数据集mAP:85.9%;
DAGM2007数据集mAP:96.7%		 TILDA数据集:34.0;香港图案数据集:21.9;DAGM2007数据集:45.2 破洞、棉球等13种 NVIDA TITAN XP GPU
15 [33] DAGM2007数据集和机器采集 DAGM2007数据集mAP:90.29%;
机器采集数据集mAP:93.27%		 — 污渍、破洞等8种 Nvidia Quadro M5000 GPU
16 [34] 天池布匹数据集 mAP:90.4% 10.3 污渍、断纬和破洞 NVIDIA TITAN XP GPU

表3

基于自动编码器的织物疵点检测方法性能"

序号 文献 数据集 精度 检测时间/(张·s-1) 疵点类型 硬件资源
1 [38] 自建数据集 mAP:98.21% 4.76 浆斑、破洞等4种 Intel i5 processor
2 [39] TILDA数据集 mAP:83.9% — 穿错、毛边等12种 GTX 980Ti Nvidia GPU
3 [40] TILDA数据集和机器采集数据集 TILDA数据集mAP:87.2%;
机器采集数据集mAP:79.2%		 — 断经、经缩等11种 Intel®Core i5-4460
4 [41] VTC-2K10.5G-C19数据集 Recall:69.7%
Precision:82.2%		 — 跳花、百脚等15种 —

表4

基于其它深度学习模型的织物疵点检测方法性能"

序号 文献 数据集 精度/% 检则时间/(张·s-1) 疵点类型 硬件资源
1 [42] TILDA数据集 — — 油污、跳纱等6种 —
2 [35] 自建数据集 mAP:94.57% 16.2 破洞、污渍等8种 Titan RTX GPU (24G)
3 [35] 自建数据集 Acc:0.95%
AP:95.8%		 — 毛边、断经等6种 NVIDIA GTX 1080TI
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