{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:17:37Z","timestamp":1778339857295,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"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":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11554-025-01669-z","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T06:35:40Z","timestamp":1743662140000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FMS-YOLO: a lightweight safety belt detection algorithm for high-altitude workers based on attention mechanism and efficient architecture"],"prefix":"10.1007","volume":"22","author":[{"given":"Fangfang","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangyu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guxue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"1669_CR1","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"1669_CR2","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.H., He, H., Zhuo, W., Wen, S., Lee, C.H., Chan, S.H.G.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12021\u201312031 (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"1669_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.autcon.2018.02.018","volume":"91","author":"W Fang","year":"2018","unstructured":"Fang, W., Ding, L., Luo, H., Love, P.E.: Falls from heights: a computer vision-based approach for safety harness detection. Autom. Constr. 91, 53\u201361 (2018)","journal-title":"Autom. Constr."},{"key":"1669_CR4","unstructured":"Gevorgyan, Z.: SIoU loss: more powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740 (2022)"},{"key":"1669_CR5","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354\u2013377 (2018)","journal-title":"Pattern Recognit."},{"key":"1669_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1669_CR7","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"1669_CR8","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1669_CR9","unstructured":"Ioffe, S.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"1669_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, Vol. 25 (2012)"},{"key":"1669_CR11","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et\u00a0al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)"},{"key":"1669_CR12","first-page":"12934","volume-title":"Advances in Neural Information Processing Systems","author":"Y Li","year":"2022","unstructured":"Li, Y., Yuan, G., Wen, Y., Hu, J., Evangelidis, G., Tulyakov, S., Wang, Y., Ren, J.: EfficientFormer: vision transformers at MobileNet speed. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol. 35, pp. 12934\u201312949. Curran Associates Inc, New York (2022)"},{"key":"1669_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1669_CR14","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1669_CR15","unstructured":"Mehta, S., Rastegari, M.: MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. arxiv: 2110.02178 (2022)"},{"key":"1669_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1669_CR17","unstructured":"Redmon, J.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"1669_CR18","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"6","key":"1669_CR19","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."},{"key":"1669_CR20","unstructured":"Ross, T.Y., Doll\u00e1r, G.: Focal loss for dense object detection. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2980\u20132988 (2017)"},{"key":"1669_CR21","doi-asserted-by":"publisher","first-page":"166603","DOI":"10.1109\/ACCESS.2021.3135662","volume":"9","author":"MZ Shanti","year":"2021","unstructured":"Shanti, M.Z., Cho, C.S., Byon, Y.J., Yeun, C.Y., Kim, T.Y., Kim, S.K., Altunaiji, A.: A novel implementation of an AI-based smart construction safety inspection protocol in the UAE. IEEE Access 9, 166603\u2013166616 (2021)","journal-title":"IEEE Access"},{"key":"1669_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106442","volume":"123","author":"D Wan","year":"2023","unstructured":"Wan, D., Lu, R., Shen, S., Xu, T., Lang, X., Ren, Z.: Mixed local channel attention for object detection. Eng. Appl. Artif. Intell. 123, 106442 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1669_CR23","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: YOLOv10: real-time end-to-end object detection. arxiv: 2405.14458 (2024)"},{"key":"1669_CR24","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":"1669_CR25","first-page":"1","volume-title":"Computer Vision - ECCV 2024","author":"CY Wang","year":"2025","unstructured":"Wang, C.Y., Yeh, I.H., Mark Liao, H.Y.: YOLOv9: learning what you want to learn using programmable gradient information. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision - ECCV 2024, pp. 1\u201321. Springer Nature, Cham (2025)"},{"key":"1669_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1669_CR27","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":"1669_CR28","doi-asserted-by":"publisher","unstructured":"Yan, W., Wang, X., Tan, S.: YOLO-DFAN: effective high-altitude safety belt detection network. Future Internet (2022). https:\/\/doi.org\/10.3390\/fi14120349","DOI":"10.3390\/fi14120349"},{"key":"1669_CR29","unstructured":"Zhang, H., Zhang, S.: Shape-IoU: more accurate metric considering bounding box shape and scale. arXiv preprint arXiv:2312.17663 (2023)"},{"key":"1669_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203\u20134212 (2018)","DOI":"10.1109\/CVPR.2018.00442"},{"key":"1669_CR31","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"},{"issue":"11","key":"1669_CR32","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212\u20133232 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"8","key":"1669_CR33","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","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. Cybern. 52(8), 8574\u20138586 (2021)","journal-title":"IEEE Trans. Cybern."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01669-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01669-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01669-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T06:23:25Z","timestamp":1746253405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01669-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1669"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01669-z","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"1 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"90"}}