{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T20:19:03Z","timestamp":1783801143020,"version":"3.55.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"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":[[2024,4]]},"DOI":"10.1007\/s00371-023-02918-7","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T20:36:49Z","timestamp":1687984609000},"page":"2309-2324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Fabric defect detection algorithm based on improved YOLOv5"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6128-3663","authenticated-orcid":false,"given":"Feng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-8392","authenticated-orcid":false,"given":"Kang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengpeng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guozheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"2918_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2014.81","author":"R Girshick","year":"2014","unstructured":"Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2014). https:\/\/doi.org\/10.1109\/cvpr.2014.81","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"2918_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/ius54386.2022.9957216","author":"R Girshick","year":"2015","unstructured":"Girshick, R.: Fast r-cnn. Proc. IEEE Int. Conf. Comput. Vis. (2015). https:\/\/doi.org\/10.1109\/ius54386.2022.9957216","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"issue":"6","key":"2918_CR3","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/tpami.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN:towards realtime objectdetection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2918_CR4","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788. https:\/\/doi.org\/10.1109\/cvpr.2016.91 (2016)","DOI":"10.1109\/cvpr.2016.91"},{"key":"2918_CR5","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D. et al.: SSD: single shot multiBox detector. In: Amsterdam: European Conference on ComputerVision, pp. 21\u201337. (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"22","key":"2918_CR6","doi-asserted-by":"publisher","first-page":"5853","DOI":"10.3390\/rs14225853","volume":"14","author":"S Lu","year":"2022","unstructured":"Lu, S., Liu, X., He, Z., Zhang, X., Liu, W., Karkee, M.: Swin-transformer-YOLOv5 for real-time wine grape bunch detection. Remote Sens. 14(22), 5853 (2022). https:\/\/doi.org\/10.3390\/rs14225853","journal-title":"Remote Sens."},{"issue":"1","key":"2918_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-01084-x","volume":"11","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Yang, X., Zhou, Y., Sun, Q., Ge, Z., Liu, D.: Real-time detection of particleboard surface defects based on improved YOLOV5 target detection. Sci. Rep. 11(1), 1\u201315 (2021). https:\/\/doi.org\/10.1038\/s41598-021-01084-x","journal-title":"Sci. Rep."},{"key":"2918_CR8","doi-asserted-by":"publisher","first-page":"2778","DOI":"10.1109\/iccvw54120.2021.00312","volume":"2021","author":"X Zhu","year":"2021","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. IEEE\/CVF Int. Conf. Comput. Vis. Workshops (ICCVW) 2021, 2778\u20132788 (2021). https:\/\/doi.org\/10.1109\/iccvw54120.2021.00312","journal-title":"IEEE\/CVF Int. Conf. Comput. Vis. Workshops (ICCVW)"},{"key":"2918_CR9","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","volume":"37","author":"W Chen","year":"2021","unstructured":"Chen, W., Huang, H., Peng, S., et al.: YOLO-face: a real-time face detector. Vis. Comput. 37, 805\u2013813 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01831-7","journal-title":"Vis. Comput."},{"key":"2918_CR10","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Liao, H.Y., Wu Y.H. et al.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1571\u20131580. https:\/\/doi.org\/10.1109\/cvprw50498.2020.00203 (2020)","DOI":"10.1109\/cvprw50498.2020.00203"},{"issue":"9","key":"2918_CR11","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/tpami.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015). https:\/\/doi.org\/10.1109\/tpami.2015.2389824","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2918_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944. (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2918_CR13","doi-asserted-by":"publisher","unstructured":"Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Shen, C.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In:\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8440\u20138449. https:\/\/doi.org\/10.1109\/iccv.2019.00853 (2019)","DOI":"10.1109\/iccv.2019.00853"},{"issue":"07","key":"2918_CR14","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Wang, P., Liu, W., et al.: Distance-IoU loss: faster and better learning for bounding box regression. Proc. AAAI Conf. Artif. Intell. 34(07), 12993\u201313000 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6999","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2918_CR15","doi-asserted-by":"publisher","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In:\u00a0Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 13713\u201313722. https:\/\/doi.org\/10.1109\/cvpr46437.2021.01350 (2021)","DOI":"10.1109\/cvpr46437.2021.01350"},{"key":"2918_CR16","unstructured":"Misra, D.M.: A self regularized non-monotonic activation function. arXiv preprint https:\/\/arxiv.org\/abs\/1908.08681 (2019). Accessed 23 Aug 2019"},{"key":"2918_CR17","unstructured":"Gevorgyan, Z.: SIoU loss: more powerful learning for bounding box regression. arXiv preprint https:\/\/arxiv.org\/abs\/2205.12740. (2022)"},{"key":"2918_CR18","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988. https:\/\/doi.org\/10.1109\/iccv.2017.324 (2017)","DOI":"10.1109\/iccv.2017.324"},{"key":"2918_CR19","doi-asserted-by":"publisher","unstructured":"Li, B., Liu, Y., Wang, X.: Gradient harmonized single-stage detector. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 8577\u20138584. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018577 (2019)","DOI":"10.1609\/aaai.v33i01.33018577"},{"key":"2918_CR20","doi-asserted-by":"publisher","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 516\u2013520. https:\/\/doi.org\/10.1145\/2964284.2967274 (2016)","DOI":"10.1145\/2964284.2967274"},{"key":"2918_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2021.3095305","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., Zuo, W.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybernet. (2021). https:\/\/doi.org\/10.1109\/tcyb.2021.3095305","journal-title":"IEEE Trans. Cybernet."},{"key":"2918_CR22","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"YF Zhang","year":"2022","unstructured":"Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022)","journal-title":"Neurocomputing"},{"key":"2918_CR23","unstructured":"Tianchi, Smart diagnosis of cloth flaw dataset[EB\/OL]. https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?dataId=79336, (2020)"},{"key":"2918_CR24","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J. Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319. (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2918_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.01155","author":"Q Wang","year":"2020","unstructured":"Wang, Q., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.01155","journal-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"key":"2918_CR26","doi-asserted-by":"publisher","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., Adam, H.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324. https:\/\/doi.org\/10.1109\/iccv.2019.00140 (2019)","DOI":"10.1109\/iccv.2019.00140"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02918-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02918-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02918-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T17:05:54Z","timestamp":1712336754000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02918-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2918"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02918-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"28 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}