{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:57:56Z","timestamp":1781812676286,"version":"3.54.5"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09632-y","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T08:02:23Z","timestamp":1711526543000},"page":"10699-10712","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FedSH: a federated learning framework for safety helmet wearing detection"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-8238","authenticated-orcid":false,"given":"Zhiqing","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"issue":"3","key":"9632_CR1","first-page":"365","volume":"40","author":"L Xiao-hui","year":"2014","unstructured":"Xiao-hui L, Xi-ning Y (2014) Skin color detection and hu moments in helmet recognition research. J East China Univ Sci Technol (Natl Sci Edn) 40(3):365\u2013370","journal-title":"J East China Univ Sci Technol (Natl Sci Edn)"},{"issue":"4","key":"9632_CR2","first-page":"69","volume":"28","author":"L Yunbo","year":"2015","unstructured":"Yunbo L, Hua H (2015) Research on monitoring of workers\u2019 helmet wearing at the construction site. Electron Sci Tech 28(4):69\u201372","journal-title":"Electron Sci Tech"},{"key":"9632_CR3","unstructured":"Wen C-Y, Chiu S-H, Liaw J-J, Lu C-P (2003) The safety helmet detection for atm\u2019s surveillance system via the modified hough transform. In: IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, 2003. Proceedings., pp. 364\u2013369. IEEE"},{"key":"9632_CR4","unstructured":"LIU G, LIU Y, WANG R (2020) Research on indoor hard hat wearing detection based on revised retinanet model. Journal of Zhejiang Wanli University"},{"key":"9632_CR5","doi-asserted-by":"crossref","unstructured":"Li S, Gao L, Yue Y (2021) Detection of helmet wearing based on improved yolo v3. In: 2021 40th Chinese Control Conference (CCC), pp. 7965\u20137970. IEEE","DOI":"10.23919\/CCC52363.2021.9549942"},{"key":"9632_CR6","doi-asserted-by":"crossref","unstructured":"Fang J, Lin X, Zhou F, Tian Y, Zhang M (2023) Safety helmet detection based on optimized yolov5. In: 2023 prognostics and health management conference (PHM), pp. 117\u2013121.. IEEE","DOI":"10.1109\/PHM58589.2023.00030"},{"issue":"16","key":"9632_CR7","doi-asserted-by":"publisher","first-page":"8268","DOI":"10.3390\/app12168268","volume":"12","author":"A Hayat","year":"2022","unstructured":"Hayat A, Morgado-Dias F (2022) Deep learning-based automatic safety helmet detection system for construction safety. Appl. Sci 12(16):8268","journal-title":"Appl. Sci"},{"key":"9632_CR8","doi-asserted-by":"publisher","first-page":"26247","DOI":"10.1109\/ACCESS.2023.3257183","volume":"11","author":"L Wang","year":"2023","unstructured":"Wang L, Zhang X, Yang H (2023) Safety helmet wearing detection model based on improved yolo-m. IEEE Access 11:26247\u201326257","journal-title":"IEEE Access"},{"key":"9632_CR9","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: artificial intelligence and statistics, pp. 1273\u20131282. PMLR"},{"issue":"2","key":"9632_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"4","key":"9632_CR11","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","volume":"35","author":"Q Li","year":"2021","unstructured":"Li Q, Wen Z, Wu Z, Hu S, Wang N, Li Y, Liu X, He B (2021) A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Tran Knowl Data Eng 35(4):3347\u201366","journal-title":"IEEE Tran Knowl Data Eng"},{"key":"9632_CR12","unstructured":"He C, Shah AD, Tang Z, Sivashunmugam DFN, Bhogaraju K, Shimpi M, Shen L, Chu X, Soltanolkotabi M, Avestimehr S (2021) Fedcv: a federated learning framework for diverse computer vision tasks. arXiv preprint arXiv:2111.11066"},{"issue":"3","key":"9632_CR13","first-page":"16","volume":"34","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Yang J, Liu M, Sun J, Gui G (2020) Federated learning based intelligent edge computing technique for video surveillance. J Commun 34(3):16\u201322","journal-title":"J Commun"},{"key":"9632_CR14","doi-asserted-by":"crossref","unstructured":"Wang S, Lu S, Cao B (2021) Medical image object detection algorithm for privacy-preserving federated learning. Journal of Computer-Aided Design & Computer Graphics\/Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao 33(10)","DOI":"10.3724\/SP.J.1089.2021.18416"},{"key":"9632_CR15","doi-asserted-by":"crossref","unstructured":"Liu Y, Huang A, Luo Y, Huang H, Liu Y, Chen Y, Feng L, Chen T, Yu H, Yang Q (2020) Fedvision: An online visual object detection platform powered by federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13172\u201313179","DOI":"10.1609\/aaai.v34i08.7021"},{"key":"9632_CR16","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"9632_CR17","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"9632_CR18","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"9632_CR19","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"9632_CR20","first-page":"3447","volume":"35","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wei X-S, Zhou B, Wu J (2021) Bag of tricks for long-tailed visual recognition with deep convolutional neural networks. Proc AAAI Conf Artif Intell 35:3447\u20133455","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"9632_CR21","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1016\/j.ins.2019.10.048","volume":"512","author":"T Pan","year":"2020","unstructured":"Pan T, Zhao J, Wu W, Yang J (2020) Learning imbalanced datasets based on smote and gaussian distribution. Inform Sci 512:1214\u20131233","journal-title":"Inform Sci"},{"key":"9632_CR22","doi-asserted-by":"crossref","unstructured":"Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: computational learning theory: second european conference, EuroCOLT\u201995 Barcelona, Spain, March 13\u201315, 1995 Proceedings 2, pp. 23\u201337. Springer","DOI":"10.1007\/3-540-59119-2_166"},{"key":"9632_CR23","unstructured":"Caton S, Haas C (2020) Fairness in machine learning: a survey. arXiv preprint arXiv:2010.04053"},{"key":"9632_CR24","unstructured":"Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: international conference on machine learning, pp. 4615\u20134625. PMLR"},{"key":"9632_CR25","unstructured":"Li T, Sanjabi M, Beirami A, Smith V (2019) Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497"},{"key":"9632_CR26","doi-asserted-by":"crossref","unstructured":"Zhao Z, Joshi G (2022) A dynamic reweighting strategy for fair federated learning. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 8772\u20138776. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746300"},{"issue":"8","key":"9632_CR27","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/TMC.2020.3045266","volume":"21","author":"Q Wu","year":"2020","unstructured":"Wu Q, Chen X, Zhou Z, Zhang J (2020) Fedhome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mobile Comput 21(8):2818\u20132832","journal-title":"IEEE Trans Mobile Comput"},{"issue":"4","key":"9632_CR28","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","volume":"35","author":"Y Chen","year":"2020","unstructured":"Chen Y, Qin X, Wang J, Yu C, Gao W (2020) Fed health: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35(4):83\u201393","journal-title":"IEEE Intell Syst"},{"key":"9632_CR29","first-page":"23","volume":"2","author":"J Guo","year":"2023","unstructured":"Guo J, Ho IW-H, Hou Y, Li Z (2023) Fedpos: a federated transfer learning framework for csi-based wi-fi indoor positioning. IEEE Syst J 2:23","journal-title":"IEEE Syst J"},{"issue":"2","key":"9632_CR30","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","volume":"8","author":"H Yang","year":"2020","unstructured":"Yang H, He H, Zhang W, Cao X (2020) Fedsteg: a federated transfer learning framework for secure image steganalysis. IEEE Trans Netw Sci Eng 8(2):1084\u20131094","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"9632_CR31","unstructured":"Arivazhagan MG, Aggarwal V, Singh AK, Choudhary S (2019) Federated learning with personalization layers. arXiv preprint arXiv:1912.00818"},{"key":"9632_CR32","unstructured":"Oh J, Kim S, Yun S-Y (2021) Fedbabu: towards enhanced representation for federated image classification. arXiv preprint arXiv:2106.06042"},{"key":"9632_CR33","first-page":"17","volume":"30","author":"V Smith","year":"2017","unstructured":"Smith V, Chiang C-K, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. Adv Neural Informat Process Syst 30:17","journal-title":"Adv Neural Informat Process Syst"},{"key":"9632_CR34","doi-asserted-by":"publisher","first-page":"9983","DOI":"10.3934\/mbe.2022466","volume":"19","author":"M Kumaresan","year":"2022","unstructured":"Kumaresan M, Kumar MS, Muthukumar N (2022) Analysis of mobility based covid-19 epidemic model using federated multitask learning. Math Biosci Eng 19:9983\u201310005","journal-title":"Math Biosci Eng"},{"key":"9632_CR35","unstructured":"Li D, Wang J (2019) Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581"},{"key":"9632_CR36","unstructured":"Yu T, Bagdasaryan E, Shmatikov V (2020) Salvaging federated learning by local adaptation. arXiv preprint arXiv:2002.04758"},{"issue":"1\u20132","key":"9632_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al (2021) Advances and open problems in federated learning. Foundat Trends Mach Learn 14(1\u20132):1\u2013210","journal-title":"Foundat Trends Mach Learn"},{"key":"9632_CR38","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"key":"9632_CR39","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"9632_CR40","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Mach Learning Syst 2:429\u2013450","journal-title":"Proc Mach Learning Syst"},{"key":"9632_CR41","first-page":"7611","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang J, Liu Q, Liang H, Joshi G, Poor HV (2020) Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv Neural Inform Process Syst 33:7611\u20137623","journal-title":"Adv Neural Inform Process Syst"},{"key":"9632_CR42","first-page":"5132","volume":"21","author":"SP Karimireddy","year":"2020","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. Int Conf Mach Learn 21:5132\u20135143","journal-title":"Int Conf Mach Learn"},{"key":"9632_CR43","doi-asserted-by":"crossref","unstructured":"Cohen G, Afshar S, Tapson J, Van\u00a0Schaik A (2017) Emnist: Extending mnist to handwritten letters. In: 2017 international joint conference on neural networks (IJCNN), pp. 2921\u20132926. IEEE","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"9632_CR44","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09632-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09632-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09632-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T20:29:44Z","timestamp":1717100984000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09632-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":44,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9632"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09632-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"4 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","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 that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}