{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:16:14Z","timestamp":1769732174683,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61673190"],"award-info":[{"award-number":["Grant No. 61673190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["Grant CCNU22JC011"],"award-info":[{"award-number":["Grant CCNU22JC011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09255-9","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T07:02:21Z","timestamp":1701932541000},"page":"3711-3723","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An embedded device-oriented fatigue driving detection method based on a YOLOv5s"],"prefix":"10.1007","volume":"36","author":[{"given":"Jiaxiang","family":"Qu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7000-1004","authenticated-orcid":false,"given":"Ziming","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yimin","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"issue":"11","key":"9255_CR1","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1109\/TITS.2018.2879609","volume":"20","author":"A Dasgupta","year":"2018","unstructured":"Dasgupta A, Rahman D, Routray A (2018) A smartphone-based drowsiness detection and warning system for automotive drivers. IEEE Trans Intell Transp Syst 20(11):4045\u20134054","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"9255_CR2","doi-asserted-by":"publisher","first-page":"2218","DOI":"10.1109\/TCBB.2020.2974944","volume":"18","author":"Z Wang","year":"2020","unstructured":"Wang Z, Hong Q, Wang X (2020) A memristive circuit implementation of eyes state detection in fatigue driving based on biological long short-term memory rule. IEEE\/ACM Trans Comput Biol Bioinf 18(6):2218\u20132229","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"1","key":"9255_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/electronics12010026","volume":"12","author":"Y Shang","year":"2023","unstructured":"Shang Y, Yang M, Cui J, Cui L, Huang Z, Li X (2023) Driver emotion and fatigue state detection based on time series fusion. Electronics 12(1):26","journal-title":"Electronics"},{"issue":"2","key":"9255_CR4","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TCDS.2019.2929858","volume":"12","author":"R Bose","year":"2019","unstructured":"Bose R, Wang H, Dragomir A, Thakor NV, Bezerianos A, Li J (2019) Regression-based continuous driving fatigue estimation: toward practical implementation. IEEE Trans Cogn Dev Syst 12(2):323\u2013331","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"9255_CR5","doi-asserted-by":"publisher","first-page":"9731","DOI":"10.1007\/s00521-019-04506-0","volume":"32","author":"JS Wijnands","year":"2020","unstructured":"Wijnands JS, Thompson J, Nice KA, Aschwanden GD, Stevenson M (2020) Real-time monitoring of driver drowsiness on mobile platforms using 3d neural networks. Neural Comput Appl 32:9731\u20139743","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9255_CR6","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3390\/s23010311","volume":"23","author":"HA Abosaq","year":"2022","unstructured":"Abosaq HA, Ramzan M, Althobiani F, Abid A, Aamir KM, Abdushkour H, Irfan M, Gommosani ME, Ghonaim SM, Shamji V et al (2022) Unusual driver behavior detection in videos using deep learning models. Sensors 23(1):311","journal-title":"Sensors"},{"issue":"4","key":"9255_CR7","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1049\/ipr2.12207","volume":"16","author":"G Zhao","year":"2022","unstructured":"Zhao G, He Y, Yang H, Tao Y (2022) Research on fatigue detection based on visual features. IET Image Proc 16(4):1044\u20131053","journal-title":"IET Image Proc"},{"issue":"16","key":"9255_CR8","doi-asserted-by":"publisher","first-page":"9961","DOI":"10.1007\/s00521-021-05764-7","volume":"33","author":"I Martinez-Alpiste","year":"2021","unstructured":"Martinez-Alpiste I, Golcarenarenji G, Wang Q, Alcaraz-Calero JM (2021) A dynamic discarding technique to increase speed and preserve accuracy for yolov3. Neural Comput Appl 33(16):9961\u20139973","journal-title":"Neural Comput Appl"},{"key":"9255_CR9","doi-asserted-by":"crossref","unstructured":"Roy AM, Bose R, Bhaduri J (2022) A fast accurate fine-grain object detection model based on yolov4 deep neural network. Neural Comput Appl 34(5):3895\u20133921","DOI":"10.1007\/s00521-021-06651-x"},{"issue":"9","key":"9255_CR10","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1049\/iet-its.2018.5590","volume":"13","author":"X Li","year":"2019","unstructured":"Li X, Hong L, Wang J-C, Liu X (2019) Fatigue driving detection model based on multi-feature fusion and semi-supervised active learning. IET Intel Transp Syst 13(9):1401\u20131409","journal-title":"IET Intel Transp Syst"},{"key":"9255_CR11","doi-asserted-by":"publisher","first-page":"6511","DOI":"10.1007\/s00521-020-05414-4","volume":"33","author":"E Ragusa","year":"2021","unstructured":"Ragusa E, Gianoglio C, Zunino R, Gastaldo P (2021) Random-based networks with dropout for embedded systems. Neural Comput Appl 33:6511\u20136526","journal-title":"Neural Comput Appl"},{"key":"9255_CR12","doi-asserted-by":"crossref","unstructured":"Amor RD, Colomer A, Monteagudo C, Naranjo V (2021) A deep embedded refined clustering approach for breast cancer distinction based on DNA methylation. Neural Comput Appl 34(13):10243\u201310255","DOI":"10.1007\/s00521-021-06357-0"},{"issue":"2","key":"9255_CR13","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/s16020242","volume":"16","author":"Z Zhang","year":"2016","unstructured":"Zhang Z, Luo D, Rasim Y, Li Y, Meng G, Xu J, Wang C (2016) A vehicle active safety model: vehicle speed control based on driver vigilance detection using wearable EEG and sparse representation. Sensors 16(2):242","journal-title":"Sensors"},{"issue":"9","key":"9255_CR14","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1016\/j.compbiomed.2006.11.010","volume":"37","author":"CD Katsis","year":"2007","unstructured":"Katsis CD, Exarchos TP, Papaloukas C, Goletsis Y, Fotiadis DI, Sarmas I (2007) A two-stage method for MUAP classification based on EMG decomposition. Comput Biol Med 37(9):1232\u20131240","journal-title":"Comput Biol Med"},{"issue":"1","key":"9255_CR15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2174\/2213385202666141218104855","volume":"2","author":"J-X Ma","year":"2014","unstructured":"Ma J-X, Shi L-C, Lu B-L (2014) An EOG-based vigilance estimation method applied for driver fatigue detection. Neurosci Biomed Eng (Discontin) 2(1):41\u201351","journal-title":"Neurosci Biomed Eng (Discontin)"},{"key":"9255_CR16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cag.2022.09.001","volume":"108","author":"H Jia","year":"2022","unstructured":"Jia H, Xiao Z, Ji P (2022) Real-time fatigue driving detection system based on multi-module fusion. Comput Graph 108:22\u201333","journal-title":"Comput Graph"},{"key":"9255_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s12239-016-0016-y","volume":"17","author":"M Wang","year":"2016","unstructured":"Wang M, Jeong N-T, Kim K-S, Choi S, Yang S, You S, Lee J, Suh M (2016) Drowsy behavior detection based on driving information. Int J Automot Technol 17:165\u2013173","journal-title":"Int J Automot Technol"},{"key":"9255_CR18","doi-asserted-by":"crossref","unstructured":"Hailin W, Hanhui L, Zhumei S (2010) Fatigue driving detection system design based on driving behavior. In: 2010 international conference on optoelectronics and image processing, vol 110. IEEE, pp 549\u2013552","DOI":"10.1109\/ICOIP.2010.101"},{"key":"9255_CR19","doi-asserted-by":"crossref","unstructured":"Sandberg D, Wahde M (2008) Particle swarm optimization of feedforward neural networks for the detection of drowsy driving. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, pp 788\u2013793","DOI":"10.1109\/IJCNN.2008.4633886"},{"key":"9255_CR20","doi-asserted-by":"crossref","unstructured":"Wang K, Ma Y, Huang J, Zhang C (2019) Driving performance of heavy-duty truck drivers under different fatigue levels at signalized intersections. In: CICTP 2019, pp 581\u2013592","DOI":"10.1061\/9780784482292.053"},{"issue":"6","key":"9255_CR21","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1177\/0361198119847985","volume":"2673","author":"AS Zandi","year":"2019","unstructured":"Zandi AS, Quddus A, Prest L, Comeau FJ (2019) Non-intrusive detection of drowsy driving based on eye tracking data. Transp Res Rec 2673(6):247\u2013257","journal-title":"Transp Res Rec"},{"key":"9255_CR22","unstructured":"Owens JM, Dingus TA, Guo F, Fang Y, Perez M, McClafferty J, Tefft BC (2018) Estimating the prevalence and crash risk of drowsy driving using data from a large-scale naturalistic driving study. Technical report"},{"issue":"11","key":"9255_CR23","doi-asserted-by":"publisher","first-page":"5609","DOI":"10.1007\/s00521-020-05342-3","volume":"33","author":"M Choudhary","year":"2021","unstructured":"Choudhary M, Tiwari V, Uduthalapally V (2021) Iris presentation attack detection based on best-k feature selection from yolo inspired roi. Neural Comput Appl 33(11):5609\u20135629","journal-title":"Neural Comput Appl"},{"issue":"10","key":"9255_CR24","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"9255_CR25","doi-asserted-by":"crossref","unstructured":"Li Z-Z, Zeng Q-H, Li X-D, Yu Y (2019) Face detection technology based on combining skin color model with improved adaboost algorithm. In: 2019 IEEE 4th international conference on signal and image processing (ICSIP). IEEE, pp 381\u2013384","DOI":"10.1109\/SIPROCESS.2019.8868565"},{"key":"9255_CR26","doi-asserted-by":"crossref","unstructured":"Qin H, Yan J, Li X, Hu X (2016) Joint training of cascaded CNN for face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3456\u20133465","DOI":"10.1109\/CVPR.2016.376"},{"key":"9255_CR27","doi-asserted-by":"crossref","unstructured":"Hai L, Guo H (2020) Face detection with improved face r-cnn training method. In: Proceedings of the 3rd international conference on control and computer vision, pp 22\u201325 (2020)","DOI":"10.1145\/3425577.3425582"},{"issue":"1","key":"9255_CR28","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/math8010093","volume":"8","author":"Z Deng","year":"2020","unstructured":"Deng Z, Yang R, Lan R, Liu Z, Luo X (2020) Se-iyolov3: an accurate small scale face detector for outdoor security. Mathematics 8(1):93","journal-title":"Mathematics"},{"key":"9255_CR29","doi-asserted-by":"crossref","unstructured":"Yang W, Jiachun Z (2018) Real-time face detection based on yolo. In: 2018 1st IEEE international conference on knowledge innovation and invention (ICKII). IEEE, pp 221\u2013224","DOI":"10.1109\/ICKII.2018.8569109"},{"key":"9255_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu R, Wang S, Yan D, Peng B, Zhang T (2022) Video face detection based on improved ssd model and target tracking algorithm. J Web Eng 21(2):545\u2013567","DOI":"10.13052\/jwe1540-9589.21218"},{"key":"9255_CR31","doi-asserted-by":"crossref","unstructured":"Jiang C, Ma H, Li L (2022) Irnet: an improved retinanet model for face detection. In: 2022 7th international conference on image, vision and computing (ICIVC). IEEE, pp 129\u2013134","DOI":"10.1109\/ICIVC55077.2022.9886975"},{"key":"9255_CR32","doi-asserted-by":"publisher","first-page":"5471","DOI":"10.1007\/s00521-019-04645-4","volume":"32","author":"J Wang","year":"2020","unstructured":"Wang J, Wang N, Li L, Ren Z (2020) Real-time behavior detection and judgment of egg breeders based on yolo v3. Neural Comput Appl 32:5471\u20135481","journal-title":"Neural Comput Appl"},{"key":"9255_CR33","doi-asserted-by":"crossref","unstructured":"Gai R, Chen N, Yuan H (2021) A detection algorithm for cherry fruits based on the improved yolo-v4 model. Neural Comput Appl 35(19):13895\u201313906","DOI":"10.1007\/s00521-021-06029-z"},{"key":"9255_CR34","unstructured":"Yang L, Zhang R-Y, Li L, Xie X (2021) Simam: a simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning. PMLR, pp 11863\u201311874"},{"key":"9255_CR35","unstructured":"Guo X, Li S, Yu J, Zhang J, Ma J, Ma L, Liu W, Ling H (2019) Pfld: a practical facial landmark detector. arXiv preprint arXiv:1902.10859"},{"key":"9255_CR36","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"9255_CR37","doi-asserted-by":"crossref","unstructured":"Yang S, Song X, Zhang L, Yu J (2017) The anti-fatigue driving system design based on the eye blink detect. In: Seventh international conference on electronics and information engineering, vol 10322. SPIE, pp. 406\u2013410","DOI":"10.1117\/12.2266074"},{"key":"9255_CR38","unstructured":"Soukupova T, Cech J (2016) Eye blink detection using facial landmarks. In: 21st computer vision winter workshop, Rimske Toplice, Slovenia, p 2"},{"key":"9255_CR39","doi-asserted-by":"crossref","unstructured":"Abtahi S, Omidyeganeh M, Shirmohammadi S, Hariri B (2014) Yawdd: a yawning detection dataset. In: Proceedings of the 5th ACM multimedia systems conference, pp 24\u201328","DOI":"10.1145\/2557642.2563678"},{"issue":"1","key":"9255_CR40","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3141\/2018-04","volume":"2018","author":"WJ Horrey","year":"2007","unstructured":"Horrey WJ, Wickens CD (2007) In-vehicle glance duration: distributions, tails, and model of crash risk. Transp Res Rec 2018(1):22\u201328","journal-title":"Transp Res Rec"},{"key":"9255_CR41","doi-asserted-by":"crossref","unstructured":"Yang S, Luo P, Loy C-C, Tang X (2016) Wider face: a face detection benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5525\u20135533","DOI":"10.1109\/CVPR.2016.596"},{"key":"9255_CR42","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s10462-018-9650-2","volume":"52","author":"A Kumar","year":"2019","unstructured":"Kumar A, Kaur A, Kumar M (2019) Face detection techniques: a review. Artif Intell Rev 52:927\u2013948","journal-title":"Artif Intell Rev"},{"key":"9255_CR43","doi-asserted-by":"crossref","unstructured":"Wu W, Qian C, Yang S, Wang Q, Cai Y, Zhou Q (2018) Look at boundary: a boundary-aware face alignment algorithm. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2129\u20132138","DOI":"10.1109\/CVPR.2018.00227"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09255-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09255-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09255-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T14:10:04Z","timestamp":1707747004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09255-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9255"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09255-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]},"assertion":[{"value":"18 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}