Abstract A deformable dense convolutional neural network is proposed for the traditional fabric defect detection method which cannot be applied to defect features with large-scale changes and small area ratios. To pay more attention to the feature information that is far away in the image and avoid capturing the texture information, deformable convolution is employed to enhance the semantic expression of the feature. By setting the respective direction x and y offsets of the convolution pixels relative to the center pixel in the convolution layer, and using backpropagation training offsets to increase the deformation adaptability of the receptive field. Meanwhile, a dense connection manner is utilized to keep the model from missing edge defect information. Finally, the classification and location detection of defects is realized based on the defect prediction and the border regression. Experimental results show that the average accuracy of the proposed approach and the standard deviation of single-type target detection accuracy is 93.53% and 2.5139, respectively, compared with other methods, it is more competitive.
Zhou J, Bu G, Wang J. Feature Extraction Using Auto-Regression Spectral Analysis for Fabric Defect Detection ［J］. Advanced Materials Research, 2011, 175-176: 366-370.
［2］
Almakky I, Palade V, Ruiz G A. Deep Convolutional Neural Networks for Text Localisation in Figures from Biomedical Literature ［C］//2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary: IEEE, 2019: 1-5.
Bodnarova A, Bennamoun M, Kubik K K. Defect Detection in Textile Materials Based on Aspects of the HVS ［C］//1998 IEEE International Conference on Systems, Man, and Cybernetic, San Diego, USA: IEEE, 1998: 4423-4428.
Guo B S, Han T J, Zhang Y, et al. Tool Wear Monitoring Method Based on One-Dimensional Residual Network with Threshold Module［J］. Acta Metrologica Sinica, 2022, 43(4): 501-506.
Liu W B, Zheng L X, Zhou K D. Detection Method of Fabric Defects Based on Frequency Domain Filtering ［J］. Journal of Huaqiao University (Natural Science), 2017, 38(4): 562-566.
［8］
Mak K L, Peng P, Yiu K F C. Fabric Defect Detection Using Morphological Filters ［J］. Image and Vision Computing, 2009, 27(10): 1585-1592.
［9］
Cohen F S, Fan Z, Attali S. Automated Inspection of Textile Fabrics Using Textural Models ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 803-810.
Jeyara P R, Samuel N E R. Computer Vision for Automatic Detection and Classification of Fabric Defect Employing Deep Learning Algorithm ［J］. International Journal of Clothing Science and Technology, 2019, 31(4): 510-521.
Liu J, Wang C, Su H, et al. Multistage GAN for Fabric Defect Detection ［J］. IEEE Transactions on Image Processing, 2020, 29: 3388-3400.
［17］
Dai J, Qi H, Xiong Y, et al. Deformable Convolutional Networks ［C］//Proceedings of the IEEE International Conference on Computer Vision (ICCV), Hawaii, USA, 2017: 764-773.
［19］
Rumelhart D E, Hinton G E, Williams R J. Learning Representations by Back-Propagating errors ［J］. Nature, 1986, 323(6088): 533-536.
TANG H, Wang K J, Li X Y, et al. Logs End Detection and Statistics by Color Difference Clustering ［J］. Acta Metrologica Sinica, 2020, 41(6): 682-688.
［20］
Lin T, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection ［C］//Proceedings of the IEEE International Conference on Computer Vision (ICCV), Hawaii, USA, 2017: 2980-2988.
Guo B S, Wu W W, Fu Q, et al. Color Difference Detection of Polycrystalline Silicon Cells Based on Support Vector Machine Classification Strategy ［J］. Acta Metrologica Sinica, 2019, 40(6): 1013-1019.
［5］
Chen J H, Anil K J. A Structural Approach to Identify Defects in Textured Images ［C］//Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, Beijing, China: IEEE, 1988: 29-32.
［13］
Li Y, Zhao W, Pan J. Deformable Patterned Fabric Defect Detection with Fisher Criterion-Based Deep Learning ［J］. IEEE Transactions on Automation Science and Engineering, 2017, 14(2): 1256-1264.
［18］
Eubeck A, Gool L V. Efficient Non-Maximum Suppression ［C］//18th International Conference on Pattern Recognition, Hong Kong, China: IEEE, 2006.
Yang Z L, Wang J L, Zhang J, et al. Data Drive Wafer Pattern Defect Pattern Recognition Method ［J］. China Mechanical Engineering, 2019, 30(2): 230-236.
［12］
Banumathi P, Nasira G M. Artificial Neural Network Techniques in Identifying Plain Woven Fabric Defects ［J］. Engineering and Technology, 2015, 9(4): 272-276.
［16］
Jing J, Ma H, Zhang H. Automatic Fabric Defect Detection Using a Deep Convolutional Neural Network ［J］. Coloration Technology, 2019, 135(3): 213-223.
Li M, Jing J F, Li P F. Yarn-dyed Fabric Defect Detection Based on GAN and Faster R-CNN ［J］. Journal of Xian Polytechnic University, 2018, 32(6): 663-669.
2023, Vol. 44 
