{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T23:51:11Z","timestamp":1781826671864,"version":"3.54.5"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Innovative Entrepreneurship Undergraduate Training Programme","award":["Grant No. Yz2025071"],"award-info":[{"award-number":["Grant No. Yz2025071"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No.62272485"],"award-info":[{"award-number":["Grant No.62272485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-49439-6","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:56:58Z","timestamp":1777046218000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PFL-YOLO: a robust framework for lightweight product damage detection with multi-scale semantic enhancement in unmanned vending systems"],"prefix":"10.1038","volume":"16","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zishi","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihao","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Mei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Biao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"49439_CR1","unstructured":"Rukundo, S. et al. A survey of challenges and sensing technologies in autonomous retail systems. arXiv preprint arxiv2503.07997 (2025)."},{"key":"49439_CR2","doi-asserted-by":"crossref","unstructured":"Butt, A.\u00a0H. & Ahmad, H. Ai-driven unmanned convenience stores: transforming retail with innovation and experiential quality. Technol. Soc. 103133 (2025).","DOI":"10.1016\/j.techsoc.2025.103133"},{"key":"49439_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/jtaer20020128","volume":"20","author":"W Hu","year":"2025","unstructured":"Hu, W., Zhang, L., Wang, Y. & Wu, J. Measuring consumer experience in community unmanned stores: Development of the ECUS-scale for omnichannel digital retail. J. Theor. Appl. Electron. Commer. Res. 20, 128 (2025).","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"49439_CR4","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s40684-019-00181-7","volume":"7","author":"D Ravimal","year":"2020","unstructured":"Ravimal, D., Kim, H., Koh, D., Hong, J. H. & Lee, S. K. Image-based inspection technique of a machined metal surface for an unmanned lapping process. Int. J. Precis. Eng. Manuf.-Green Technol. 7, 547\u2013557 (2020).","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"key":"49439_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124635","volume":"255","author":"R Pietrini","year":"2024","unstructured":"Pietrini, R., Paolanti, M., Mancini, A., Frontoni, E. & Zingaretti, P. Shelf management: A deep learning-based system for shelf visual monitoring. Expert Syst. Appl. 255, 124635 (2024).","journal-title":"Expert Syst. Appl."},{"key":"49439_CR6","doi-asserted-by":"publisher","first-page":"99570","DOI":"10.1109\/ACCESS.2024.3429555","volume":"12","author":"C You","year":"2024","unstructured":"You, C. & Kong, H. Improved steel surface defect detection algorithm based on YOLOv8. IEEE Access 12, 99570\u201399577 (2024).","journal-title":"IEEE Access"},{"key":"49439_CR7","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R. & Le, Q.\u00a0V. EfficientDet: scalable and efficient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10781\u201310790 (2020).","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"49439_CR8","doi-asserted-by":"crossref","unstructured":"Xu, K. et al. Learning in the frequency domain. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 1740\u20131749 (2020).","DOI":"10.1109\/CVPR42600.2020.00181"},{"key":"49439_CR9","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F. & Li, X.\u00a0F. Frequency channel attention networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Vol.\u00a010 (2021).","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"49439_CR10","unstructured":"Chen, L., Gu, L., Li, L., Yan, C. & Fu, Y. Frequency dynamic convolution for dense image prediction (fdconv). In Advances in Neural Information Processing Systems (NeurIPS) 1\u201311 (2020)."},{"key":"49439_CR11","doi-asserted-by":"publisher","first-page":"7058","DOI":"10.1109\/TMM.2024.3359769","volume":"26","author":"Y Huang","year":"2024","unstructured":"Huang, Y. et al. Wavedm: Wavelet-based diffusion models for image restoration. IEEE Trans. Multimed. 26, 7058\u20137073 (2024).","journal-title":"IEEE Trans. Multimed."},{"key":"49439_CR12","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, L., Guo, S. & Lai, Z. Wavelet integrated CNNs for noise-robust image classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 7245\u20137254 (2020).","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"49439_CR13","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin, I. O. et al. MLP-mixer: an all-MLP architecture for vision. Adv. Neural Inf. Process. Syst. 34, 24261\u201324272 (2021).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"49439_CR14","doi-asserted-by":"crossref","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 (CVPR) 779\u2013788 (2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"49439_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/app15020733","volume":"15","author":"A Mjahad","year":"2025","unstructured":"Mjahad, A., Polo-Aguado, A., Llorens-Serrano, L. & Rosado-Mu\u00f1oz, A. Optimizing image feature extraction with convolutional neural networks for chicken meat detection applications. Appl. Sci. 15, 733 (2025).","journal-title":"Appl. Sci."},{"key":"49439_CR16","doi-asserted-by":"crossref","unstructured":"Xiao, Z. & Wang, X. Event-based video super-resolution via state space models (MambaOutblock). In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2025).","DOI":"10.1109\/CVPR52734.2025.01172"},{"key":"49439_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100648","volume":"53","author":"R Thirunavukarasu","year":"2024","unstructured":"Thirunavukarasu, R. & Kotei, E. A comprehensive review on transformer network for natural and medical image analysis. Comput. Sci. Rev. 53, 100648 (2024).","journal-title":"Comput. Sci. Rev."},{"key":"49439_CR18","doi-asserted-by":"crossref","unstructured":"Carion, F. et al. End-to-end object detection with transformers. In Proceedings of the European Conference on Computer Vision (ECCV) 213\u2013229 (2020).","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"49439_CR19","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arxiv2010.11929 (2020)."},{"key":"49439_CR20","doi-asserted-by":"publisher","first-page":"8515","DOI":"10.1016\/j.asr.2025.04.009","volume":"75","author":"H Song","year":"2025","unstructured":"Song, H. et al. CMKD-Net: A cross-modal knowledge distillation method for remote sensing image classification. Adv. Space Res. 75, 8515\u20138534 (2025).","journal-title":"Adv. Space Res."},{"key":"49439_CR21","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.3390\/sym17071002","volume":"17","author":"H Song","year":"2025","unstructured":"Song, H. et al. Symmetrical learning and transferring: Efficient knowledge distillation for remote sensing image classification. Symmetry 17, 1002 (2025).","journal-title":"Symmetry"},{"key":"49439_CR22","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1111\/phor.12489","volume":"39","author":"H Song","year":"2024","unstructured":"Song, H., Yuan, Y., Ouyang, Z., Yang, Y. & Xiang, H. Quantitative regularization in robust vision transformer for remote sensing image classification. Photogramm. Rec. 39, 340\u2013372 (2024).","journal-title":"Photogramm. Rec."},{"key":"49439_CR23","doi-asserted-by":"crossref","unstructured":"Shi, D. TransNeXt: robust foveal visual perception for vision transformers. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 17773\u201317783 (2024).","DOI":"10.1109\/CVPR52733.2024.01683"},{"key":"49439_CR24","unstructured":"Mehta, S. & Rastegari, M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arxiv2110.02178 (2021)."},{"key":"49439_CR25","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2024.1452019","volume":"18","author":"H Chen","year":"2024","unstructured":"Chen, H. & Yue, X. Swimtrans net: A multimodal robotic system for swimming action recognition driven via Swin-Transformer. Frontiers in Neurorobotics 18, 1452019 (2024).","journal-title":"Frontiers in Neurorobotics"},{"key":"49439_CR26","doi-asserted-by":"crossref","unstructured":"Chen, L., Gu, L., Li, L., Yan, C. & Fu, Y. Frequency dynamic convolution for dense image prediction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 30178\u201330188 (2025).","DOI":"10.1109\/CVPR52734.2025.02809"},{"key":"49439_CR27","doi-asserted-by":"crossref","unstructured":"Yu, W. & Wang, X. MambaOut : Do we really need mamba for vision? In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 4484\u20134496 (2025).","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"49439_CR28","unstructured":"Gu, A. & Dao, T. Mamba: linear-time sequence modeling with selective state spaces. In Proceedings of the First Conference on Language Modeling (2024)."},{"key":"49439_CR29","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2020","unstructured":"He, Y., Song, K., Meng, Q. & Yan, Y. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69, 1493\u20131504. https:\/\/doi.org\/10.1109\/TIM.2019.2915404 (2020).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"49439_CR30","doi-asserted-by":"publisher","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D. & Steger, C. MVTec AD\u2014a comprehensive real-world dataset for unsupervised anomaly detection. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 9584\u20139592. https:\/\/doi.org\/10.1109\/CVPR.2019.00982 (2019).","DOI":"10.1109\/CVPR.2019.00982"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49439-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49439-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49439-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:58:32Z","timestamp":1781823512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49439-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["49439"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-49439-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"12 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":3,"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":"Competing interests"}},{"value":"Not applicable. This study does not involve human participants or animal experiments.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"19075"}}