{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:34:26Z","timestamp":1779294866075,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"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":[[2025,8]]},"DOI":"10.1007\/s11554-025-01720-z","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T14:17:24Z","timestamp":1751379444000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FAN-YOLO: Real-time driver behavior detection based on full-layer aggregation network of YOLO"],"prefix":"10.1007","volume":"22","author":[{"given":"Xinsu","family":"Gan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lidong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"1720_CR1","unstructured":"World Health Organization. Regional approach to the decade of action for road safety 2021-2030.\u201d Regional approach to the decade of action for road safety 2021-2030 (2022)"},{"issue":"1","key":"1720_CR2","first-page":"4125865","volume":"2019","author":"Hesham M Eraqi","year":"2019","unstructured":"Eraqi, Hesham M., et al.: Driver distraction identification with an ensemble of convolutional neural networks. Journal of advanced transportation 2019(1), 4125865 (2019)","journal-title":"Journal of advanced transportation"},{"key":"1720_CR3","unstructured":"Berri, Rafael A., et al.: \u201cA pattern recognition system for detecting use of mobile phones while driving.\u201d 2014 International conference on computer vision theory and applications (VISAPP). Vol. 2. IEEE (2014)"},{"key":"1720_CR4","doi-asserted-by":"crossref","unstructured":"Seshadri, K., Juefei-Xu, F., Pal, D. K., Savvides, M., Thor, C. P.: Driver cell phone usage detection on strategic highway research program (SHRP2) face view videos. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 35-43 (2015)","DOI":"10.1109\/CVPRW.2015.7301397"},{"key":"1720_CR5","unstructured":"Abouelnaga, Y., H.M. Eraqi, Moustafa, M.N.: \u201cReal-time distracted driver posture classification. arXiv 2017.\u201d arXiv preprint arXiv:1706.09498 (2017)"},{"key":"1720_CR6","doi-asserted-by":"crossref","unstructured":"Tran, Duy, et al.: \u201cReal-time detection of distracted driving based on deep learning.\u201d IET Intelligent Transport Systems 12.10 :1210-1219 (2018)","DOI":"10.1049\/iet-its.2018.5172"},{"key":"1720_CR7","doi-asserted-by":"crossref","unstructured":"Wu, Chenxia, et al.:\u201cWatch-n-patch: unsupervised learning of actions and relations.\u201d IEEE transactions on pattern analysis and machine intelligence 40.2 : 467-481 (2017)","DOI":"10.1109\/TPAMI.2017.2679054"},{"key":"1720_CR8","doi-asserted-by":"crossref","unstructured":"Baheti, B., Gajre, S., Talbar, S.: Detection of distracted driver using convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 1032-1038 (2018).","DOI":"10.1109\/CVPRW.2018.00150"},{"issue":"4","key":"1720_CR9","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1007\/s10489-019-01603-4","volume":"50","author":"M Lu","year":"2020","unstructured":"Lu, M., Hu, Y., Lu, X.: Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals. Applied Intelligence 50(4), 1100\u20131111 (2020)","journal-title":"Applied Intelligence"},{"key":"1720_CR10","doi-asserted-by":"crossref","unstructured":"Majdi, M.S., Ram, S., Gill, J.T., Rodr\u00edguez, J.J.: Drive-net: Convolutional network for driver distraction detection. In 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 1-4. IEEE (2018)","DOI":"10.1109\/SSIAI.2018.8470309"},{"key":"1720_CR11","first-page":"1","volume":"73","author":"J Shen","year":"2024","unstructured":"Shen, J., Liu, N., Sun, H., Li, D., Zhang, Y.: An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features. IEEE Transactions on Instrumentation and Measurement 73, 1\u201316 (2024)","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1720_CR12","doi-asserted-by":"crossref","unstructured":"Poon, Y.S., Kao, C.Y., Wang, Y.K., Hsiao, C.C., Hung, M.Y., Wang, Y.C., Fan, C.P.: Driver distracted behavior detection technology with YOLO-based deep learning networks. In 2021 IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-ASIA), pp. 01-05. IEEE. (2021)","DOI":"10.1109\/ISPCE-ASIA53453.2021.9652435"},{"issue":"1","key":"1720_CR13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.18280\/ijsse.110104","volume":"11","author":"Belmekki Ghizlene Amira","year":"2021","unstructured":"Amira, Belmekki Ghizlene, Zoulikha, Mekkakia Maaza, Hector, Pomares: Driver drowsiness detection and tracking based on YOLO with Haar cascades and ERNN. International Journal of Safety and Security Engineering 11(1), 35\u201342 (2021)","journal-title":"International Journal of Safety and Security Engineering"},{"issue":"9","key":"1720_CR14","doi-asserted-by":"publisher","first-page":"15898","DOI":"10.1109\/TITS.2022.3146271","volume":"23","author":"Long Qin","year":"2022","unstructured":"Qin, Long, et al.: ID-YOLO: Real-time salient object detection based on the driver\u2019s fixation region. IEEE Transactions on Intelligent Transportation Systems 23(9), 15898\u201315908 (2022)","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"1720_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, Keke, et al.: \u201cAn Improved Distraction Behavior Detection Algorithm Based on YOLOv5.\u201d Computers, Materials Continua 81.2 (2024)","DOI":"10.32604\/cmc.2024.056863"},{"key":"1720_CR16","doi-asserted-by":"crossref","unstructured":"Debsi, Ali, et al.: \u201cDriver distraction and fatigue detection in images using ME-YOLOv8 algorithm.\u201d IET Intelligent Transport Systems 18.10: 1910-1930 (2024)","DOI":"10.1049\/itr2.12560"},{"key":"1720_CR17","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. 2117-2125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1720_CR18","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. 8759-8768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1720_CR19","first-page":"1904","volume":"37.9","author":"He Kaiming","year":"2015","unstructured":"Kaiming, He., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37.9, 1904\u20131916 (2015)","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"1720_CR20","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, pp. 779-788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1720_CR21","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. 7263-7271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1720_CR22","unstructured":"Redmon, Joseph, Farhadi, Ali: \u201cYolov3: An incremental improvement.\u201d arXiv preprint arXiv:1804.02767 (2018)"},{"key":"1720_CR23","unstructured":"Bochkovskiy, Alexey, Wang, Chien-Yao, Liao, Hong-Yuan Mark: \u201cYolov4: Optimal speed and accuracy of object detection.\u201d arXiv preprint arXiv:2004.10934 (2020)"},{"key":"1720_CR24","unstructured":"Jocher, Glenn, et al.: \u201cultralytics\/yolov5: v6. 1-tensorrt, tensorflow edge tpu and openvino export and inference.\u201d Zenodo (2022)"},{"key":"1720_CR25","unstructured":"Li, Chuyi, et al.: \u201cYOLOv6: A single-stage object detection framework for industrial applications.\u201d arXiv preprint arXiv:2209.02976 (2022)"},{"key":"1720_CR26","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. 7464-7475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1720_CR27","doi-asserted-by":"crossref","unstructured":"Varghese, R., Sambath, M.: Yolov8: A novel object detection algorithm with enhanced performance and robustness. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), pp. 1-6. IEEE (2024)","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"1720_CR28","doi-asserted-by":"crossref","unstructured":"Wang, C. Y., Yeh, I. H., Mark Liao, H. Y.: Yolov9: Learning what you want to learn using programmable gradient information. In European conference on computer vision (pp. 1-21). Cham: Springer Nature Switzerland (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"1720_CR29","unstructured":"Wang, Ao, et al.: \u201cYolov10: Real-time end-to-end object detection.\u201d Advances in Neural Information Processing Systems 37: 107984-108011 (2025)"},{"key":"1720_CR30","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 13733-13742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1720_CR31","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1580-1589 (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"2","key":"1720_CR32","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TPAMI.2022.3164083","volume":"45","author":"Y Li","year":"2022","unstructured":"Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 45(2), 1489\u20131500 (2022)","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"1720_CR33","doi-asserted-by":"crossref","unstructured":"Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., Hariri, B.: YawDD: A yawning detection dataset. In Proceedings of the 5th ACM multimedia systems conference, pp. 24-28 (2014)","DOI":"10.1145\/2557642.2563678"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01720-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01720-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01720-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T16:58:51Z","timestamp":1757350731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01720-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["1720"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01720-z","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"24 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that consent to publish has been obtained for any images containing potential human faces, in accordance with ethical guidelines.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"139"}}