{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T21:22:00Z","timestamp":1776720120806,"version":"3.51.2"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00371-024-03716-5","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T13:21:34Z","timestamp":1731417694000},"page":"5205-5221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Multi-scale defect detection for plaid fabrics using scale sequence feature fusion and triple encoding"],"prefix":"10.1007","volume":"41","author":[{"given":"Zewei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"3716_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939843","author":"B Shi","year":"2019","unstructured":"Shi, B., Liang, J., Di, L., Chen, C., Hou, Z.: Fabric defect detection via low-rank decomposition with gradient information. IEEE Access. (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2939843","journal-title":"IEEE Access."},{"key":"3716_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2667890","author":"L Tong","year":"2017","unstructured":"Tong, L., Wong, W.K., Kwong, C.K.: Fabric defect detection for apparel industry: a nonlocal sparse representation approach. IEEE Access (2017). https:\/\/doi.org\/10.1109\/ACCESS.2017.2667890","journal-title":"IEEE Access"},{"key":"3716_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(01)00188-1","author":"D Chetverikov","year":"2002","unstructured":"Chetverikov, D., Hanbury, A.: Finding defects in texture using regularity and local orientation. Pattern Recognit. (2002). https:\/\/doi.org\/10.1016\/S0031-3203(01)00188-1","journal-title":"Pattern Recognit."},{"issue":"2","key":"3716_CR4","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/28.993164","volume":"38","author":"A Kumar","year":"2002","unstructured":"Kumar, A., Pang, G.K.: Defect detection in textured materials using Gabor filters. IEEE Trans. Ind. Appl. 38(2), 425\u2013440 (2002)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"3716_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-02040-y","author":"G Liu","year":"2022","unstructured":"Liu, G., Li, F.: Fabric defect detection based on low-rank decomposition with structural constraints. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-020-02040-y","journal-title":"Vis. Comput."},{"key":"3716_CR6","doi-asserted-by":"publisher","DOI":"10.1108\/IJCST-12-2015-0134","author":"C Li","year":"2016","unstructured":"Li, C., Yang, R., Liu, Z., Gao, G., Liu, Q.: Fabric defect detection via learned dictionary-based visual saliency. Int. J. Cloth. Sci. Technol. (2016). https:\/\/doi.org\/10.1108\/IJCST-12-2015-0134","journal-title":"Int. J. Cloth. Sci. Technol."},{"key":"3716_CR7","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxy121","author":"M Dhivya","year":"2019","unstructured":"Dhivya, M., Renuka Devi, M.: Detection of structural defects in fabric parts using a novel edge detection method. Comput. J. (2019). https:\/\/doi.org\/10.1093\/comjnl\/bxy121","journal-title":"Comput. J."},{"key":"3716_CR8","doi-asserted-by":"crossref","unstructured":"Hamdi, A. A., Sayed, M. S., Fouad, M. M., Hadhoud, M. M.: Fully automated approach for patterned fabric defect detection. In: 2016 Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), pp. 48\u201351 (2016)","DOI":"10.1109\/JEC-ECC.2016.7518965"},{"key":"3716_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10212652","author":"Z Peng","year":"2021","unstructured":"Peng, Z., Gong, X., Wei, B., Xu, X., Meng, S.: Automatic unsupervised fabric defect detection based on self-feature comparison. Electronics (2021). https:\/\/doi.org\/10.3390\/electronics10212652","journal-title":"Electronics"},{"key":"3716_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3085669","author":"J Li","year":"2022","unstructured":"Li, J., Chen, J., Sheng, B., Li, P., Yang, P., Feng, D.D., Qi, J.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Ind. Inform. (2022). https:\/\/doi.org\/10.1109\/TII.2021.3085669","journal-title":"IEEE Trans. Ind. Inform."},{"key":"3716_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3229031","author":"J Wang","year":"2022","unstructured":"Wang, J., Xu, G., Li, C., Gao, G., Wu, Q.: Sddet: An enhanced encoder\u2013decoder network with hierarchical supervision for surface defect detection. IEEE Sens. J. (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3229031","journal-title":"IEEE Sens. J."},{"key":"3716_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02702-z","author":"L Dai","year":"2024","unstructured":"Dai, L., et al.: A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med. (2024). https:\/\/doi.org\/10.1038\/s41591-023-02702-z","journal-title":"Nat. Med."},{"key":"3716_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3079311","author":"B Sheng","year":"2022","unstructured":"Sheng, B., Li, P., Ali, R., Chen, C.L.P.: Improving video temporal consistency via broad learning system. IEEE Trans Cybern. (2022). https:\/\/doi.org\/10.1109\/TCYB.2021.3079311","journal-title":"IEEE Trans Cybern."},{"key":"3716_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3144890","author":"N Jiang","year":"2023","unstructured":"Jiang, N., Sheng, B., Li, P., Lee, T.Y.: PhotoHelper: portrait photographing guidance via deep feature retrieval and fusion. IEEE Trans. Multimed. (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3144890","journal-title":"IEEE Trans. Multimed."},{"key":"3716_CR15","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, C., Liu, Z., Dong, Y., Huang, Y.: Combing deep and handcrafted features for NTV-NRPCA based fabric defect detection, In Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV 2019, Xi\u2019an, China, November 8\u201311, 2019, Proceedings, Part III 2, pp. 479-490 (2019)","DOI":"10.1007\/978-3-030-31726-3_41"},{"key":"3716_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3021482","author":"Y Dong","year":"2020","unstructured":"Dong, Y., Wang, J., Li, C., Liu, Z., Xi, J., Zhang, A.: Fusing multilevel deep features for fabric defect detection based NTV-RPCA. IEEE Access (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3021482","journal-title":"IEEE Access"},{"key":"3716_CR17","unstructured":"Roesler, U.: Defect detection of fabrics by image processing. Melliand Texilber. (1992)"},{"issue":"1","key":"3716_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/S0262-8856(99)00009-8","volume":"18","author":"D-M Tsai","year":"1999","unstructured":"Tsai, D.-M., Hsieh, C.-Y.: Automated surface inspection for directional textures. Image Vis. Comput. 18(1), 49\u201362 (1999)","journal-title":"Image Vis. Comput."},{"key":"3716_CR19","doi-asserted-by":"publisher","first-page":"116827","DOI":"10.1016\/j.eswa.2022.116827","volume":"198","author":"Z Pourkaramdel","year":"2022","unstructured":"Pourkaramdel, Z., Fekri-Ershad, S., Nanni, L.: Fabric defect detection based on completed local quartet patterns and majority decision algorithm. Expert Syst. Appl. 198, 116827 (2022)","journal-title":"Expert Syst. Appl."},{"key":"3716_CR20","doi-asserted-by":"crossref","unstructured":"Xie, H., Yang, D., Sun, N., Chen, Z., Zhang, Y.: Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern recognition. (2019)","DOI":"10.1016\/j.patcog.2018.07.031"},{"key":"3716_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, S., Li, C., Ding, S., Dong, Y.: Fabric defects detection based on SSD. In: Proceedings of the 2nd International Conference on Graphics and Signal Processing, pp. 74\u201378 (2018)","DOI":"10.1145\/3282286.3282300"},{"key":"3716_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2019.102144","author":"W Du","year":"2019","unstructured":"Du, W., Shen, H., Fu, J., Zhang, G., He, Q.: Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning. NDT E Int. (2019). https:\/\/doi.org\/10.1016\/j.ndteint.2019.102144","journal-title":"NDT E Int."},{"key":"3716_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108690","author":"J Li","year":"2024","unstructured":"Li, J., Kang, X.: Mobile-YOLO: an accurate and efficient three-stage cascaded network for online fiberglass fabric defect detection. Eng. Appl. Artif. Intell. (2024). https:\/\/doi.org\/10.1016\/j.engappai.2024.108690","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3716_CR24","first-page":"155892502412582","volume":"19","author":"R Yang","year":"2024","unstructured":"Yang, R., Guo, N., Tian, B., Wang, J., Liu, S., Yu, M.: Fabric defect detection via saliency model based on adjacent context coordination and transformer. J. Eng. Fibers Fabr. 19, 15589250241258272 (2024)","journal-title":"J. Eng. Fibers Fabr."},{"key":"3716_CR25","unstructured":"Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. (2022) arXiv:2206.02424"},{"key":"3716_CR26","unstructured":"Andrew, G., Menglong, Z.: Efficient convolutional neural networks for mobile vision applications, mobilenets. (2017) arXiv1704.04861"},{"key":"3716_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"3716_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105057","author":"M Kang","year":"2024","unstructured":"Kang, M., Ting, C.-M., Ting, F.F., Phan, R.C.-W.: ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation. Image Vis. Comput. (2024). https:\/\/doi.org\/10.1016\/j.imavis.2024.105057","journal-title":"Image Vis. Comput."},{"key":"3716_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection, In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16965\u201316974","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"3716_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12993\u201313000 (2020)","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"3716_CR31","unstructured":"Zhang, H., Zhang, S.: Shape-IoU: more accurate metric considering bounding box shape and scale. (2023) arXiv:2312.17663"},{"issue":"1","key":"3716_CR32","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1108\/IJCST-11-2021-0165","volume":"35","author":"S Zhou","year":"2023","unstructured":"Zhou, S., Zhao, J., Shi, Y.S., Wang, Y.F., Mei, S.Q.: Research on improving YOLOv5s algorithm for fabric defect detection. Int. J. Cloth. Sci. Technol. 35(1), 88\u2013106 (2023)","journal-title":"Int. J. Cloth. Sci. Technol."},{"key":"3716_CR33","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.: Ssd: Single shot multibox detector, In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21-37 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3716_CR34","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q.: Centernet: Keypoint triplets for object detection, In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"3716_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/rs11050531","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., Wei, S.: Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sens. (2019). https:\/\/doi.org\/10.3390\/rs11050531","journal-title":"Remote Sens."},{"key":"3716_CR36","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q. V.: Efficientdet: Scalable and efficient object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"3716_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios, In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"3716_CR38","doi-asserted-by":"crossref","unstructured":"Cheng, P., Tang, X., Liang, W., Li, Y., Cong, W., Zang, C.: Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery, In: International Conference on Image and Graphics, pp. 53\u201365 (2023)","DOI":"10.1007\/978-3-031-46311-2_5"},{"key":"3716_CR39","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"3716_CR40","doi-asserted-by":"crossref","unstructured":"Lalinia, M., Sahafi, A.: Colorectal polyp detection in colonoscopy images using yolo-v8 network. Signal, Image Video Processing. (2024)","DOI":"10.1007\/s11760-023-02835-1"},{"key":"3716_CR41","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.01440-23","author":"E Guemas","year":"2024","unstructured":"Guemas, E., Routier, B., Ghelfenstein-Ferreira, T., Cordier, C., Hartuis, S., Marion, B., Bertout, S., Varlet-Marie, E., Costa, D., Pasquier, G.: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation. Microbiol. Spectr. (2024). https:\/\/doi.org\/10.1128\/spectrum.01440-23","journal-title":"Microbiol. Spectr."},{"issue":"6","key":"3716_CR42","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03716-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T10:02:40Z","timestamp":1745488960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03716-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["3716"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03716-5","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"4 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The datasets generated during the current study are openly available at .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}