{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:42:06Z","timestamp":1769316126537,"version":"3.49.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shaanxi Provincial Department of Science and Technology General Project","award":["2024JC-YBMS-491"],"award-info":[{"award-number":["2024JC-YBMS-491"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s11760-025-04462-4","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:31:03Z","timestamp":1752139863000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Occlusive and multi-scale clothing detection algorithm based on improved YOLOv8"],"prefix":"10.1007","volume":"19","author":[{"given":"Meihua","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxiao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyue","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"issue":"5","key":"4462_CR1","first-page":"2076","volume":"10","author":"Y Jo","year":"2020","unstructured":"Jo, Y., Wi, J., Kim, M., et al.: Flexible fashion product retrieval using multimodality-based deep learning. Appl. Sciences-Basel. 10(5), 2076\u20133417 (2020)","journal-title":"Appl. Sciences-Basel"},{"issue":"9","key":"4462_CR2","doi-asserted-by":"publisher","first-page":"3782","DOI":"10.3390\/app11093782","volume":"11","author":"CH Lee","year":"2021","unstructured":"Lee, C.H., Lin, C.W.: A two-phase fashion apparel detection method based on YOLOv4. Appl. Sciences-Basel. 11(9), 3782 (2021)","journal-title":"Appl. Sciences-Basel"},{"key":"4462_CR3","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.neucom.2019.03.011","volume":"341","author":"LL Liu","year":"2019","unstructured":"Liu, L.L., Zhang, H.J., Ji, Y.Z., et al.: Toward AI fashion design: An Attribute-GAN model for clothing match. Neurocomputing. 341, 156\u2013167 (2019)","journal-title":"Neurocomputing"},{"issue":"4","key":"4462_CR4","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1108\/IJCST-02-2022-0017","volume":"35","author":"WH Luo","year":"2023","unstructured":"Luo, W.H., Zhong, Y.Q.: DO-VTON: A details-oriented virtual try-on network. Int. J. Cloth. Sci. Technol. 35(4), 565\u2013580 (2023)","journal-title":"Int. J. Cloth. Sci. Technol."},{"key":"4462_CR5","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"4462_CR6","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 real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4462_CR7","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"4462_CR8","unstructured":"Li, C., Li, L., Jiang, H., et al.: YOLOv6: A single-stage object detection framework for industrial applications (2022). arXiv preprint arXiv:2209.02976"},{"key":"4462_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., et al.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9756\u20139765 (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"4462_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yang, C., Li, Q., et al.: Disentangle your dense object detector arXiv preprint arXiv:2107.02963 (2021)","DOI":"10.1145\/3474085.3475351"},{"key":"4462_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"3","key":"4462_CR12","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3390\/agriculture14030353","volume":"14","author":"DZ Sun","year":"2024","unstructured":"Sun, D.Z., Zhang, K., Zhong, H.S., et al.: Efficient tobacco pest detection in complex environments using an enhanced YOLOv8 model. Agriculture-Basel. 14(3), 353 (2024)","journal-title":"Agriculture-Basel"},{"key":"4462_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 9992\u201310002 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"4462_CR14","unstructured":"Wang, C., He, W., Nie, Y., et al.: Gold-YOLO: Efficient object detector via gather-and-distribute mechanism (2023). arXiv preprint arXiv:2309.11331"},{"key":"4462_CR15","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., Wang, Y.: Receptive field block net for accurate and fast object detection. In: 2018 European Conference on Computer Vision (ECCV). pp. 404\u2013419 (2018)","DOI":"10.1007\/978-3-030-01252-6_24"},{"issue":"6","key":"4462_CR16","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/LGRS.2020.2993652","volume":"18","author":"JM Topple","year":"2021","unstructured":"Topple, J.M., Fawcett, J.A.: MiNet: Efficient deep learning automatic target recognition for small autonomous vehicles. IEEE Geosci. Remote Sens. Lett. 18(6), 1014\u20131018 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"4462_CR17","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1016\/j.patcog.2007.05.017","volume":"41","author":"KQ Huang","year":"2008","unstructured":"Huang, K.Q., Wang, L.S., Tan, T.N., et al.: A real-time object detecting and tracking system for outdoor night surveillance. Pattern Recogn. 41(1), 432\u2013444 (2008)","journal-title":"Pattern Recogn."},{"key":"4462_CR18","doi-asserted-by":"crossref","unstructured":"Shou, Y.T., Meng, T., Ai, W., et al.: Object detection in medical images based on hierarchical transformer and mask mechanism. Computational Intelligence and Neuroscience. 5863782(2022) (2022)","DOI":"10.1155\/2022\/5863782"},{"key":"4462_CR19","doi-asserted-by":"publisher","first-page":"2881","DOI":"10.1007\/s10845-024-02387-2","volume":"36","author":"S Zhao","year":"2025","unstructured":"Zhao, S., Zhong, R.Y., Xu, C., et al.: A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing. J. Intell. Manuf. 36, 2881\u20132896 (2025)","journal-title":"J. Intell. Manuf."},{"key":"4462_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, S.X., Zhong, Y., Wang, J.L., et al.: Unsupervised fabric defects detection based on Spatial domain saliency and features clustering. Comput. Ind. Eng. 185 (2023)","DOI":"10.1016\/j.cie.2023.109681"},{"issue":"2","key":"4462_CR21","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1109\/TII.2022.3188349","volume":"19","author":"JL Wang","year":"2022","unstructured":"Wang, J.L., Zhao, S.X., Xu, C.Q., Zhang, J., et al.: Brain-inspired interpretable network pruning for smart vision-based defect detection equipment. IEEE Trans. Industr. Inf. 19(2), 1666\u20131673 (2022)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4462_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., Xiao, T., Jiang, Y., et al.: Repulsion loss: detecting pedestrians in a crowd arXiv preprint arXiv:1711.07752 (2017)","DOI":"10.1109\/CVPR.2018.00811"},{"key":"4462_CR23","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.patcog.2018.08.018","volume":"86","author":"CL Zhou","year":"2019","unstructured":"Zhou, C.L., Yuan, J.S.: Multi-label learning of part detectors for occluded pedestrian detection. Pattern Recogn. 86, 99\u2013111 (2019)","journal-title":"Pattern Recogn."},{"issue":"5","key":"4462_CR24","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.3390\/s21051820","volume":"21","author":"XT Shao","year":"2021","unstructured":"Shao, X.T., Wang, Q., Yang, W., et al.: Multi-scale feature pyramid network: A heavily occluded pedestrian detection network based on ResNet. Sensors. 21(5), 1820 (2021)","journal-title":"Sensors"},{"key":"4462_CR25","doi-asserted-by":"publisher","first-page":"108436","DOI":"10.1016\/j.compeleceng.2022.108436","volume":"104","author":"X Chen","year":"2022","unstructured":"Chen, X., Li, Y.J., Nakatoh, Y.: Pyramid attention object detection network with multi-scale feature fusion. Comput. Electr. Eng. 104, 108436 (2022)","journal-title":"Comput. Electr. Eng."},{"issue":"7","key":"4462_CR26","doi-asserted-by":"publisher","first-page":"755","DOI":"10.3390\/rs11070755","volume":"11","author":"XD Zhang","year":"2019","unstructured":"Zhang, X.D., Zhu, K., Chen, G.Z., et al.: Geospatial object detection on high resolution remote sensing imagery based on double multi-scale feature pyramid network. Remote Sens. 11(7), 755 (2019)","journal-title":"Remote Sens."},{"issue":"1","key":"4462_CR27","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s10489-022-03459-7","volume":"53","author":"HY Wei","year":"2023","unstructured":"Wei, H.Y., Zhang, Q.Q., Qian, Y.R., et al.: MTSDet: multi-scale traffic sign detection with attention and path aggregation. Appl. Intell. 53(1), 238\u2013250 (2023)","journal-title":"Appl. Intell."},{"key":"4462_CR28","unstructured":"Naseer, M., Ranasinghe, K., Khan, S., et al.: Intriguing properties of vision transformers arXiv preprint arXiv:2105.10497 (2021)"},{"key":"4462_CR29","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H.F., et al.: Path Aggregation Network for Instance Segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"4462_CR30","unstructured":"Luo, W., Li, Y., Urtasun, R., et al.: Understanding the effective receptive field in deep convolutional neural networks arXiv preprint arXiv:1701.04128 (2017)"},{"key":"4462_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, S., Yang, F., Kiapour, H.: M.,: ModaNet: a large-scale street fashion dataset with polygon annotations (2018). arXiv preprint arXiv:1807.0139","DOI":"10.1145\/3240508.3240652"},{"key":"4462_CR32","doi-asserted-by":"crossref","unstructured":"Ge, Y.Y., Zhang, R.M., Wang, X.G., et al.: Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition. pp. 5337\u20135345 (2019)","DOI":"10.1109\/CVPR.2019.00548"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04462-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04462-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04462-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T03:21:48Z","timestamp":1757215308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04462-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":32,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["4462"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04462-4","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"13 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"859"}}