{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:25:49Z","timestamp":1775539549102,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976105"],"award-info":[{"award-number":["61976105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.<\/jats:p>","DOI":"10.3390\/s22134718","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"4718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Online Detection of Fabric Defects Based on Improved CenterNet with Deformable Convolution"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5177-0812","authenticated-orcid":false,"given":"Jun","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Textile Science & Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruru","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Textile Science & Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Textile Science & Engineering, Jiangnan University, No. 1800, Lihu Avenue, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1177\/0040517520935984","article-title":"Fabric defect detection based on a deep convolutional neural network using a two-stage strategy","volume":"91","author":"Xiang","year":"2021","journal-title":"Text. Res. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.engappai.2008.05.006","article-title":"Fabric defect detection based on multiple fractal features and support vector data description","volume":"22","author":"Bu","year":"2009","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","unstructured":"Mak, K., Peng, P., and Lau, H. (2005, January 14\u201317). Optimal morphological filter design for fabric defect detection. Proceedings of the 2005 IEEE International Conference on Industrial Technology, Hong Kong, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2017.01.039","article-title":"Fabric defect inspection based on lattice segmentation and Gabor filtering","volume":"238","author":"Jia","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1016\/S0031-3203(02)00017-1","article-title":"Optimal Gabor filters for textile flaw detection","volume":"35","author":"Bodnarova","year":"2002","journal-title":"Pattern Recognit."},{"key":"ref_6","first-page":"137","article-title":"Fabric defect detection of statistic aberration feature based on GMRF model","volume":"34","author":"Yang","year":"2013","journal-title":"J. Text. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/TMM.2014.2298832","article-title":"Texture modeling using contourlets and finite mixtures of generalized Gaussian distributions and applications","volume":"16","author":"Allili","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_8","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. In Proceeding of the Advances in Neural information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tao, X., Zhang, D., Ma, W., Liu, X., and Xu, D. (2018). Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl. Sci., 8.","DOI":"10.3390\/app8091575"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TIP.2019.2959741","article-title":"Multistage GAN for fabric defect detection","volume":"29","author":"Liu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1111\/cote.12394","article-title":"Automatic fabric defect detection using a deep convolutional neural network","volume":"135","author":"Jing","year":"2019","journal-title":"Color. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TASE.2016.2520955","article-title":"Deformable patterned fabric defect detection with fisher criterion-based deep learning","volume":"14","author":"Li","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 20\u201323). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_19","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 11\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceeding of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_22","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_23","first-page":"1558925020908268","article-title":"Fabric defect detection using the improved YOLOv3 model","volume":"15","author":"Jing","year":"2020","journal-title":"J. Eng. Fibers Fabr."},{"key":"ref_24","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (2022). CenterNet++ for Object Detection. arXiv."},{"key":"ref_25","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 21\u201326). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 21\u201326). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_28","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2019, January 10\u201312). Deep Equilibrium Models. Proceedings of the Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_30","unstructured":"Tian, C. (2020, October 21). Smart Diagnosis of Cloth Flaw Dataset. Available online: https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?dataId=79336."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (, January Aug). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15\u201321). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_37","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021, January 4). Deformable DETR: Deformable Transformers for End-to-End Object Detection. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_38","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_40","unstructured":"Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., and Ling, H. (February, January 27). M2det: A single-shot object detector based on multi-level feature pyramid network. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_41","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Polosukhin, I. (2017, January 8). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4718\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:37:48Z","timestamp":1760139468000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134718"],"URL":"https:\/\/doi.org\/10.3390\/s22134718","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,22]]}}}