Defect detection is crucial for controlling product quality in the process of textile production. However, for existing detection techniques, there are still challenges in identifying different forms of defect and small defects within the same category. To address this issue, we propose a fabric defect detection model called MAS-YOLO. This model is based on YOLOv8n and incorporates several key innovations. First, we designed a multi-branch coordinate attention module to capture direction and position information. Second, we designed an adaptive weighted downsampling module based on grouped convolution, which emphasizes defective features and reduces background interference using weighting features. Finally, we introduced sliding loss to address the imbalance between easy and difficult samples. The experimental results show that the mean average precisions for a customized fabric defect dataset and the AliCloud Tianchi dataset were 96.3% and 51.6%, respectively, that is, 6.9% and 7.8% higher, respectively, than the original YOLOv8n. The detection speeds using the GTX1050ti graphics card and RTX3070ti graphics card are 57.3 frames per second (fps) and 154.3 fps, respectively; this can meet the real-time requirements of defect detection in most industrial sites and provide technical support for the application of lightweight network models in the industry.
KangX.Research on fabric defect detection method based on lightweight network. J Eng Fibers Fabr2024;
19: 15589250241232153.
2.
OjalaTPietikainenM, andHarwoodD. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of 12th international conference on pattern recognition, Jerusalem, 9–13 October 1994, vol. 581, pp. 582–585.
3.
DalalNTriggsB. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), San Diego, CA, USA, 20–25 June 2005, vol. 881, pp. 886–893. Piscataway, NJ: IEEE.
4.
LoweDG. Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, Kerkyra, Greece, 20–27 September 1999, vol. 1152, pp. 1150–1157. Piscataway, NJ: IEEE.
5.
AmariSWuS.Improving support vector machine classifiers by modifying kernel functions. Neural Networks1999;
12: 783–789.
6.
CaoYMiaoQ-GLiuJ-C, et al.
Advance and prospects of AdaBoost algorithm. Acta Autom Sin2013;
39: 745–758.
7.
GaoSChuM, andZhangL.A detection network for small defects of steel surface based on YOLOv7. Digital Signal Process2024;
149: 104484.
8.
ZhongZLiCChenM, et al.
Micro LED defect detection with self-attention mechanism-based neural network. Digital Signal Process2024;
149: 104474.
9.
BhattPMMalhanRKRajendranP, et al.
Image-based surface defect detection using deep learning: A review. J Comput Inf Sci Eng2021;
21: 040801.
10.
KahramanYDurmuşoğluA.Deep learning-based fabric defect detection: A review. Text Res J2023;
93: 1485–1503.
11.
LuY.Artificial intelligence: A survey on evolution, models, applications and future trends. J Manage Anal2019;
6: 1–29.
12.
HuangYJingJ, andWangZ.Fabric defect segmentation method based on deep learning. IEEE Trans Instrum Meas2021;
70: 5005715.
13.
GirshickRDonahueJDarrellT, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, 23–28 June 2014, pp. 580–587. Piscataway, NJ: IEEE.
14.
GirshickR. Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, Chile, 7–13 December 2015, pp. 1440–1448. Piscataway, NJ: IEEE.
15.
RenSHeKGirshickR, et al.
Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell2017;
39: 1137–1149.
16.
LiuWAnguelovDErhanD, et al. SSD: Single Shot MultiBox Detector. In: LeibeBMatasJSebeN, et al. (eds.) Computer vision—ECCV 2016: 14th European conference, Amsterdam, the Netherlands, October 11–14, 2016, proceedings, Part I.
Cham:
Springer International Publishing, 2016, pp. 21–37.
17.
WangCBochkovskiyA, andLiaoHM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: 2023 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Vancouver, BC, Canada, 17–24 June 2023, pp. 7464–7475. Piscataway, NJ: IEEE.
18.
RedmonJFarhadiA. YOLO9000: Better, faster, stronger. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, pp. 6517–6525. Piscataway, NJ: IEEE.
19.
RedmonJDivvalaSGirshickR, et al. You only look once: Unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, pp. 779–788. Piscataway, NJ: IEEE.
20.
LiCLiLJiangH, et al.
YOLOv6: A single-stage object detection framework for industrial applications. arXiv2022; arXiv:220902976.
21.
RedmonJFarhadiA. Yolov3: An incremental improvement. arXiv2018; arXiv: 180402767.
22.
BochkovskiyAWangC-Y, andLiaoH-YM.Yolov4: Optimal speed and accuracy of object detection. arXiv2020; arXiv:200410934.
23.
WangHWangJBaiK, et al.
Centered multi-task generative adversarial network for small object detection. Sensors2021;
21: 5194.
24.
YuZHuangHChenW, et al.
YOLO-FaceV2: A scale and occlusion aware face detector. Pattern Recognit2024;
155: 110714.
25.
HeDWenJ, andLaiZ.Textile fabric defect detection based on improved faster R-CNN. AATCC J Res2021;
8: 82–90.
26.
RevathyGKalaivaniR.Fabric defect detection and classification via deep learning-based improved mask RCNN. Signal, Image Video Process2024;
18: 2183–2193.
27.
KailasamKSinghJGopikaGS, et al. Fabric defect detection using deep learning. In: 2024 3rd international conference on sentiment analysis and deep learning (ICSADL), Bhimdatta, Nepal, 13–14 March 2024, pp. 399–405. Piscataway, NJ: IEEE.
28.
HouWWenSLiP, et al. Surface defect detection of fabric based on improved faster R-CNN. In: 2023 IEEE international conference on sensors, electronics and computer engineering (ICSECE), Jinzhou, China, 18–20 August 2023, pp. 600–604. Piscataway, NJ: IEEE.
29.
JingJZhuoDZhangH, et al.
Fabric defect detection using the improved YOLOv3 model. J Eng Fibers Fabr2020;
15: 1558925020908268.
30.
XieHZhangY, andWuZ.An improved fabric defect detection method based on SSD. AATCC J Res2021;
8: 181–190.
31.
DiLDengSLiangJ, et al.
Context receptive field and adaptive feature fusion for fabric defect detection. Soft Comput2023;
27: 13421–13434.
32.
LinGLiuKXiaX, et al.
An efficient and intelligent detection method for fabric defects based on improved YOLOv5. Sensors2023;
23: 97.
33.
ZhouSZhaoJShiYS, et al.
Research on improving YOLOv5s algorithm for fabric defect detection. Int J Clothing Sci Technol2023;
35: 88–106.
34.
GuoYKangXLiJ, et al.
Automatic fabric defect detection method using AC-YOLOv5. Electronics2023;
12: 2950.
35.
KangXLiJ.AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric. Text Res J2024;
94: 225–245.
36.
LiuSHuangLZhaoY, et al.
Lightweight single shot multi-box detector: A fabric defect detection algorithm incorporating parallel dilated convolution and dual channel attention. Text Res J2024;
94: 209–224.
37.
WangYXuYYuZ, et al.
Color-patterned fabric defect detection based on the improved YOLOv5s model. Text Res J2023;
93: 4792–4803.
38.
GuMZhouJPanR, et al. DF-YOLO: An attempt on enhancing generalization in fabric defect detection based on YOLO network. Text Res J. Epub ahead of print 24 September 2024. DOI: 10.1177/00405175241269163.
39.
YangHJingJWangZ, et al.
YOLOV4-TinyS: A new convolutional neural architecture for real-time detection of fabric defects in edge devices. Text Res J2024;
94: 49–68.
40.
ChenCZhouQLiS, et al.
Fabric defect detection algorithm based on improved YOLOv8. Text Res J2025;
95: 235–251.
41.
HouQZhouD, andFengJ. Coordinate attention for efficient mobile network design. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Nashville, TN, USA, 2 November 2021, pp. 13713–13722. Piscataway, NJ: IEEE.
42.
SelvarajuRRCogswellMDasA, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), Venice, Italy, 22–29 October 2017, pp. 618–626. Piscataway, NJ: IEEE.
