结合注意力机制与自适应记忆性融合网络的纺织品瑕疵检测

doi:10.16451/j.cnki.issn1003-6059.202206006

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摘要为了解决纺织生产工艺中瑕疵检测成本较高、精度较低、速度较慢等问题,文中提出结合注意力机制与自适应记忆性融合网络的纺织品瑕疵检测模型.首先,在YOLOv5骨干网络中引入改进的注意力模块,构建特征提取网络,增强模型对纺织品瑕疵特征的提取能力.然后,为了增强浅层定位信息的传递效应和有效缓解特征融合时产生的混叠效应,提出自适应记忆性融合网络,在提高特征尺度不变性的同时,将骨干网络中的特征信息融入特征融合层.最后,引入CDIoU(Control Distance Intersection over Union)损失函数,提高检测精度.在ZJU-Leaper纺织品瑕疵数据集和天池纺织品瑕疵数据集上的实验表明,文中模型具有较高的检测精度和较快的检测速度.

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	邓世爽
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	姜代红

关键词 ：纺织品瑕疵检测, 注意力机制, YOLOv5, 自适应记忆性融合网络

Abstract：To solve the problems of high cost, low precision and slow speed of defect detection in textile production process, a textile defect detection model combining attention mechanism and adaptive memory fusion network is proposed. Firstly, the improved attention module is introduced into the YOLOv5 backbone network to build a SCNet feature extraction network and improve the ability to extract textile defect features. Then, an adaptive memory feature fusion network is proposed to enhance the transfer of shallow localization information and effectively mitigate the confounding effect generated during feature fusion. Thus, the feature scale invariance is improved while feature information in the backbone network is incorporated into the feature fusion layer. Finally, the control distance intersection over union loss function is introduced into the proposed model to increase the detection accuracy. Experiments on ZJU-Leaper and Tianchi textile defect datasets show that the proposed model generates higher detection accuracy and speed.

Key words： Textile Defect Detection Attention Mechanism YOLOv5 Adaptive Memory Fusion Network

收稿日期: 2021-12-03

ZTFLH:

TP 391

基金资助:2021道路交通安全公安部重点实验室开放课题(No.2021ZDSYSKFKT04)、江苏省研究生科研与实践创新计划项目(No.SJCX22_1105)资助

通讯作者: 狄岚,硕士,副教授,主要研究方向为模式识别、数字图像处理.E-mail:dilan@jiangnan.edu.cn.

作者简介: 邓世爽,硕士研究生,主要研究方向为计算机视觉.E-mail:799596785@qq.com.
梁久祯,博士,教授,主要研究方向为计算机视觉.E-mail:jzliang@cczu.edu.cn.
姜代红,博士,教授,主要研究方向为图像处理、计算机视觉.E-mail:jdh@xzit.edu.cn.

引用本文:

邓世爽, 狄岚, 梁久祯, 姜代红. 结合注意力机制与自适应记忆性融合网络的纺织品瑕疵检测[J]. 模式识别与人工智能, 2022, 35(6): 536-547. DENG Shishuang, DI Lan, LIANG Jiuzhen, JIANG Daihong. Textile Defect Detection Combining Attention Mechanism and Adaptive Memory Fusion Network. Pattern Recognition and Artificial Intelligence, 2022, 35(6): 536-547.

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[1] LI C, LI J, LI Y F, et al. Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art. Security and Communication Networks, 2021. DOI: 10.1155/2021/9948808.
[2] WANG W Z, DENG N, XIN B J. Sequential Detection of Image Defects for Patterned Fabrics. IEEE Access, 2020, 8: 174751-174762.
[3] LI F, YUAN L N, ZHANG K, et al. A Defect Detection Method for Unpatterned Fabric Based on Multidirectional Binary Patterns and the Gray-Level Co-occurrence Matrix. Textile Research Journal, 2020, 90(7/8): 776-796.
[4] LIU Z F, YAN L, LI C L, et al. Fabric Defect Detection Based on Sparse Representation of Main Local Binary Pattern. International Journal of Clothing Science and Technology, 2017, 29(3): 282-293.
[5] ZHAO C F, CHEN Y, MA J C. Fabric Defect Detection Algorithm Based on PHOG and SVM. Indian Journal of Fibre & Textile Research, 2020, 45(1): 123-126.
[6] 纪旋,梁久祯,侯振杰,等.基于模板校正与低秩分解的纺织品瑕疵检测方法.模式识别与人工智能, 2019, 32(3): 268-277.
(JI X, LIANG J Z, HOU Z J, et al. Fabric Defect Detection Based on Template Correction and Low-Rank Decomposition. Pattern Re-cognition and Artificial Intelligence, 2019, 32(3): 268-277.)
[7] SHI B S, LIANG J Z, DI L, et al. Fabric Defect Detection via Low-Rank Decomposition with Gradient Information. IEEE Access, 2019, 7: 130423-130437.
[8] 龙涵彬,狄岚,梁久祯.基于畸变校正与视觉显著特征的纺织品瑕疵检测.模式识别与人工智能, 2020, 33(12): 1122-1134.
(LONG H B, DI L, LIANG J Z. Fabric Defect Detection Based on Distortion Correction and Visual Salient Features. Pattern Recognition and Artificial Intelligence, 2020, 33(12): 1122-1134.)
[9] LIU J H, WANG C Y, SU H, et al. Multistage GAN for Fabric Defect Detection. IEEE Transactions on Image Processing, 2019, 29: 3388-3400.
[10] MEI S, WANG Y D, WEN G J. Automatic Fabric Defect Detection with a Multi-scale Convolutional Denoising Autoencoder Network Model. Sensors, 2018, 18(4). DOI: 10.3390/s18041064.
[11] WU Y, ZHANG X D, FANG F Z. Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization. Sensors, 2020, 20(3). DOI: 10.3390/s20030871.
[12] HU G H, HUANG J F, WANG Q H, et al. Unsupervised Fabric Defect Detection Based on a Deep Convolutional Generative Adversarial Network. Textile Research Journal, 2020, 90(3/4): 247-270.
[13] XIE H S, WU Z S. A Robust Fabric Defect Detection Method Based on Improved RefineDet. Sensors, 2020, 20(15). DOI: 10.3390/s20154260.
[14] WU X W, SAHOO D, HOI S C H. Recent Advances in Deep Learning for Object Detection. Neurocomputing, 2020, 396: 39-64.
[15] 蔡兆信,李瑞新,戴逸丹,等.基于Faster RCNN的布匹瑕疵识别系统.计算机系统应用, 2021, 30(2): 83-88.
(CAI Z X, LI R X, DAI Y D, et al. Fabric Defect Recognition System Based on Faster RCNN. Computer Systems and Applications, 2021, 30(2): 83-88.)
[16] HE X Y, WU L M, SONG F Y, et al. Research on Fabric Defect Detection Based on Deep Fusion DenseNet-SSD Network // Proc of the International Conference on Wireless Communication and Sensor Networks. New York, USA: ACM, 2020: 60-64.
[17] KONG W J, ZHANG H H, JING J F, et al. A Defect Detection Method for Diverse Texture Fabric Based on CenterNet // Proc of the 17th International Conference on Intelligent Computing Theories and Application. Berlin, Germany: Springer, 2021: 655-664.
[18] 孟志青,邱健数.基于级联卷积神经网络的复杂花色布匹瑕疵检测算法.模式识别与人工智能, 2020, 33(12): 1135-1144.
(MENG Z Q, QIU J S. Defect Detection Algorithm of Complex Pattern Fabric Based on Cascaded Convolution Neural Network. Pattern Recognition and Artificial Intelligence, 2020, 33(12): 1135-1144.)
[19] ZHANG H W, ZHANG L J, LI P F, et al. Yarn-Dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks // Proc of the 7th IEEE Data Driven Control and Learning Systems Conference. Washington, USA: IEEE, 2018: 170-174.
[20] 谢景洋,王巍,刘婷.基于YOLOv3算法的不同主干网络对织物瑕疵检测.测控技术, 2021, 40(3): 61-66, 95.
(XIE J Y, WANG W, LIU T. Fabric Surface Defect Detection Based on YOLOv3 with Different Backbone Networks. Measurement & Control Technology, 2021, 40(3): 61-66, 95.)
[21] ZHANG C L, LI T H, ZHANG W B. The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLOV4. Agronomy, 2022, 12(1). DOI: 10.3390/agronomy12010066.
[22] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: A New Backbone That Can Enhance Learning Capability of CNN // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition Workshops. Washington, USA: IEEE, 2020: 1571-1580.
[23] LIU S T, HUANG D, WANG Y H.Learning Spatial Fusion for Single-Shot Object Detection [C/OL]. [2021-11-15].https://arxiv.org/pdf/1911.09516.pdf.
[24] CHEN D, MIAO D Q.Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression[C/OL]. [2021-11-15].https://arxiv.org/pdf/2103.11696.pdf.
[25] LIU S, QI L, QIN H F, et al. Path Aggregation Network for Instance Segmentation // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 8759-8768.
[26] STERGIOU A, POPPE R, KALLIATAKIS G. Refining Activation Downsampling with SoftPool // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 10357-10366.
[27] GUO D, WANG H, WANG M. Dual Visual Attention Network for Visual Dialog // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2019: 4989-4995.
[28] GUO D, WANG H, WANG S H, et al. Textual-Visual Reference-Aware Attention Network for Visual Dialog. IEEE Transactions on Image Processing, 2020, 29: 6655-6666.
[29] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional Block Attention Module // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 3-19.
[30] ZHANG C K, FENG S Z, WANG X L Q, et al. ZJU-Leaper: A Benchmark Dataset for Fabric Defect Detection and a Comparative Study. IEEE Transactions on Artificial Intelligence, 2020, 1(3): 219-232.
[31] ZHAO Z Y, YANG X X, ZHOU Y C, et al. Real-Time Detection of Particleboard Surface Defects Based on Improved YOLOV5 Target Detection. Scientific Reports, 2021, 11(1). DOI: 10.1038/s41598-021-01084-x.
[32] GE Z, LIU S T, WANG F, ,et al. YOLOX: Exceeding YOLO Series in 2021[C/OL]. [2021-11-17]. https://arxiv.org/pdf/2107.08430v1.pdf.
[33] WANG C Y, YEH I H, LIAO H Y M. You Only Learn One Re-presentation: Unified Network for Multiple Tasks[C/OL].[2021-11-17]. https://arxiv.org/pdf/2105.04206.pdf.