When the conventional semantic segmentation method is applied to fabric defect detection, the omission factor of small size defects is relatively high, and the network model with larger depth is easy to lose the features of small size defects and has poor real-time performance. To address these problems, we propose two sensitive semantic segmentations, ClothNet based on deep feature fusion and ClothNet-tiny based on atrous spatial pyramid pooling. First, in ClothNet, deep and shallow features are fused to compensate for the information loss caused by pooling. Second, ClothNet-tiny is designed to improve the detection speed. Finally, an adaptive loss function for defect size, namely weighted dice loss is proposed. The results on the validation set show that ClothNet achieves 78.8% Mean Intersection over Union mean. Compared to fully convolutional networks, ClothNet reduces memory consumption by 28% and ClothNet-tiny by 77%.
SongSJingJHuangY, et al.
EfficientDet for fabric defect detection based on edge computing. Journal of Engineered Fibers and Fabrics2021;
16: 1–13.
3.
ZhuZHanGJiaG, et al.
Modified densenet for automatic fabric defect detection with edge computing for minimizing latency. IEEE Internet Things J2020;
7: 9623–9636.
4.
WuYZhouJAkankwasaNT, et al.
Fabric texture representation using the stable learned discrete cosine transform dictionary. Text Res J2019;
89: 294–310.
5.
LiuJWangCSuH, et al.
Multistage GAN for fabric defect detection. IEEE Trans Image Process2019;
29: 3388–3400.
6.
DlaminiSKaoC-YSuS-L, et al.
Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model. Textile Research Journal2022;
92: 675–690.
7.
LiCLiJLiY, et al.
Fabric defect detection in textile manufacturing: a survey of the state of the art2021;
2021: 1–13.
8.
KumariRBandaraGDissanayakeMBJJoSSylvester Matrix-Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics2021;
2021: 1–11.
9.
JingJYangPLiP, et al.
Supervised defect detection on textile fabrics via optimal Gabor filter. J Indust Text2013;
44: 40–57.
10.
SchneiderDMerhofD.Blind weave detection for woven fabrics. Pattern Anas Appl2015;
18: 725–737.
11.
LiCGaoGLiuZ, et al.
Fabric defect detection based on biological vision modeling. IEEE Access2018;
6: 27659–27670.
12.
ShiBLiangJDiL, et al.
Fabric defect detection via low-rank decomposition with gradient information. IEEE Access2019;
7: 130423–130437.
13.
LiuZZhangCLiC, et al.
Fabric defect recognition using optimized neural networks2019;
14: 1–10.
14.
SimonyanKZissermanA.Very deep convolutional networks for large-scale image recognition. arXiv preprint2014; arXiv:1409.1556.
15.
JingJFMaHZhangHH.Automatic fabric defect detection using a deep convolutional neural network. Colorat Technol2019;
135: 213–223.
16.
LiuZLiuSLiC, et al. Fabric defects detection based on SSD. In: Proceedings of the 2nd International Conference on Graphics and Signal Processing, October 6–8, 2018, NSW, Sydney, Australia, pp. 74–78.
17.
LiFLiF.Bag of tricks for fabric defect detection based on Cascade R-CNN. Text Res J2020;
91: 599–612.
18.
BodlaNSinghBChellappaR, et al. Soft-NMS – improving object detection with one line of code. In: Proceedings of the IEEE International Conference on Computer Vision, October 22–29, 2017, Venice, Italy, pp. 5561–5569.
19.
GeWYangSYuY. Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18–22, 2018, Salt Lake City, Utah, USA, pp. 1277–1286.
20.
HoC-CChouW-CSuE.Deep convolutional neural network optimization for defect detection in fabric inspection. Sensors2021;
21: 7074.
21.
YangYSoattoS. FDA: Fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 14–19, 2020, Online, pp. 4085–4095.
22.
RomeraEAlvarezJMBergasaLM, et al.
Erfnet: Efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans Intell Transport Syst2017;
19: 263–272.
23.
JingJWangZRätschM, et al.
Mobile-Unet: An efficient convolutional neural network for fabric defect detection. 2022;
92: 30–42.
24.
HaQWatanabeKKarasawaT, et al. MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sept 24–28. 2017, Vancouver, BC, Canada, pp. 5108–5115. IEEE.
25.
LiuYLiuYDingL.Scene classification based on two-stage deep feature fusion. IEEE Geosci Remote Sens Lett2017;
15: 183–186.
26.
IoffeSSzegedyC. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, July 6–11, 2015. Lille France,, pp. 448–456. PMLR.
27.
XuBWangNChenT, et al.
Empirical evaluation of rectified activations in convolutional network. arXiv preprint2015; arXiv:1505.00853.
28.
HeKZhangXRenS, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016. June 26th–July 1st, Las Vegas, Nevada, USA, pp. 770–778.
29.
YuFKoltunV.Multi-scale context aggregation by dilated convolutions. arXiv preprint2015; arXiv:1511.07122.
30.
LinT-YGoyalPGirshickR, et al. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, October 22–29, 2017, Venice, Italy, pp. 2980–2988.
31.
LiXSunXMengY, et al.
Dice loss for data-imbalanced NLP tasks. arXiv preprint2019; arXiv:1911.02855.
PrecheltL. Early stopping-but when? In: Neural Networks: Tricks of the trade. Berlin, Heidelberg: Springer, 1998, pp. 55–69.
34.
HavelkaANagyLTunákM, et al. Testing the effect of textile materials on car seat comfort in real traffic. Journal of Industrial Textiles2021;
51: 740–767.
