{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:35:35Z","timestamp":1771634135748,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T00:00:00Z","timestamp":1608595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Distracted driving behavior has become a leading cause of vehicle crashes. This paper proposes a data augmentation method for distracted driving detection based on the driving operation area. First, the class activation mapping method is used to show the key feature areas of driving behavior analysis, and then the driving operation areas are detected by the faster R-CNN detection model for data augmentation. Finally, the convolutional neural network classification mode is implemented and evaluated to detect the original dataset and the driving operation area dataset. The classification result achieves a 96.97% accuracy using the distracted driving dataset. The results show the necessity of driving operation area extraction in the preprocessing stage, which can effectively remove the redundant information in the images to get a higher classification accuracy rate. The method of this research can be used to detect drivers in actual application scenarios to identify dangerous driving behaviors, which helps to give early warning of unsafe driving behaviors and avoid accidents.<\/jats:p>","DOI":"10.3390\/fi13010001","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T12:42:28Z","timestamp":1608640948000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Data Augmentation Approach to Distracted Driving Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3183-0982","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Graduate School of Computer Applied Technology, University of Science and Technology of China, Hefei 230026, China"},{"name":"High Magnetic Field Laboratory of Anhui Province, Hefei 230031, China"}]},{"given":"ZhongCheng","family":"Wu","sequence":"additional","affiliation":[{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Graduate School of Computer Applied Technology, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Fang","family":"Li","sequence":"additional","affiliation":[{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"High Magnetic Field Laboratory of Anhui Province, Hefei 230031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1321-6022","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"High Magnetic Field Laboratory of Anhui Province, Hefei 230031, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety 2018: Summary, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/15660970500086130","article-title":"Global collaboration on road traffic injury prevention","volume":"12","author":"Peden","year":"2005","journal-title":"Int. J. Inj. Control Saf. Promot."},{"key":"ref_3","unstructured":"Singh, S. (2015). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey, National Highway Traffic Safety Administration."},{"key":"ref_4","first-page":"6","article-title":"Distraction and Risk","volume":"130","author":"Vasilash","year":"2018","journal-title":"Automot. Des. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.trf.2012.05.004","article-title":"Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior","volume":"15","author":"Kaber","year":"2012","journal-title":"Transp. Res. Part F-Traffic Psychol. Behav."},{"key":"ref_6","unstructured":"Strickland, D. (2020, December 21). How Autonomous Vehicles Will Shape the Future of Surface Transportation, Available online: https:\/\/www.govinfo.gov\/content\/pkg\/CHRG-113hhrg85609\/pdf\/CHRG-113hhrg85609.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, D. (2020, January 11\u201312). Driver status monitoring and early warning system based on multi-sensor fusion. Proceedings of the 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos.","DOI":"10.1109\/ICITBS49701.2020.00013"},{"key":"ref_8","unstructured":"Yanfei, L., Yu, Z., Junsong, L., Jing, S., Feng, F., and Jiangsheng, G. (2013, January 21\u201326). Towards Early Status Warning for Driver\u2019s Fatigue Based on Cognitive Behavior Models. Proceedings of the Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: 4th International Conference, DHM 2013, Held as Part of HCI International 2013, Las Vegas, NV, USA."},{"key":"ref_9","unstructured":"Liu, X., Zhu, Y.D., and Fujimura, K. (2002, January 3\u20136). Real-time pose classification for driver monitoring. Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, Singapore."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Eren, H., Celik, U., and Poyraz, M. (2007, January 13\u201315). Stereo vision and statistical based behaviour prediction of driver. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290191"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1049\/iet-its.2011.0116","article-title":"Recognition of driving postures by contourlet transform and random forests","volume":"6","author":"Zhao","year":"2012","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1016\/j.engappai.2012.09.018","article-title":"Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier","volume":"25","author":"Zhao","year":"2012","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","unstructured":"Chihang, Z., Bailing, Z., Jie, L., Jie, H., Tao, L., and Xiaoxiao, Z. (2011, January 12\u201315). Classification of Driving Postures by Support Vector Machines. Proceedings of the 2011 Sixth International Conference on Image and Graphics, Hefei, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"S175","DOI":"10.1007\/s00521-012-1057-4","article-title":"Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers","volume":"22","author":"Zhao","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yan, C., Coenen, F., and Zhang, B.L. (2014). Driving Posture Recognition by Joint Application of Motion History Image and Pyramid histogram of Oriented Gradients. Int. J. Veh. Technol., 846\u2013847.","DOI":"10.4028\/www.scientific.net\/AMR.846-847.1102"},{"key":"ref_16","unstructured":"Yan, C., Zhang, B., and Coenen, F. (2015, January 15\u201317). Driving Posture Recognition by Convolutional Neural Networks. Proceedings of the 2015 11th International Conference on Natural Computation (Icnc), Zhangjiajie, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yan, S., Teng, Y., Smith, J.S., and Zhang, B. (2016, January 13\u201315). Driver Behavior Recognition Based on Deep Convolutional Neural Networks. Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (Icnc-Fskd), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603248"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"144648","DOI":"10.1109\/ACCESS.2019.2945136","article-title":"3DCNN-Based Real-Time Driver Fatigue Behavior Detection in Urban Rail Transit","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18580","DOI":"10.1109\/ACCESS.2020.2968464","article-title":"A Deep-Learning-Based Scheme for Detecting Driver Cell-Phone Use","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, Y.C., Lu, M.Q., and Lu, X.B. (2020). Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network. Signal Process. Image Commun., 81.","DOI":"10.1016\/j.image.2019.115697"},{"key":"ref_21","unstructured":"Kaggle (2020, December 21). State Farm Distracted Driver Detection. Available online: https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection\/data."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alotaibi, M., and Alotaibi, B. (2019). Distracted driver classification using deep learning. Signal Image Video Process.","DOI":"10.1007\/s11760-019-01589-z"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1007\/s10489-019-01603-4","article-title":"Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals","volume":"50","author":"Lu","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Valeriano, L.C., Napoletano, P., and Schettini, R. (2018, January 2\u20135). Recognition of driver distractions using deep learning. Proceedings of the 2018 IEEE 8th International Conference on Consumer Electronics, Berlin, Germany.","DOI":"10.1109\/ICCE-Berlin.2018.8576183"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Moslemi, N., Azmi, R., and Soryani, M. (2019, January 6\u20137). Driver Distraction Recognition using 3D Convolutional Neural Networks. Proceedings of the 2019 4th International Conference on Pattern Recognition and Image Analysis, Tehran, Iran.","DOI":"10.1109\/PRIA.2019.8786012"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Eraqi, H.M., Abouelnaga, Y., Saad, M.H., and Moustafa, M.N. (2019). Driver Distraction Identification with an Ensemble of Convolutional Neural Networks. J. Adv. Transp.","DOI":"10.1155\/2019\/4125865"},{"key":"ref_27","unstructured":"Abouelnaga, Y., Eraqi, H.M., and Moustafa, M.N. (2017). Real-time Distracted Driver Posture Classification. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Baheti, B., Gajre, S., Talbar, S., and IEEE (2018, January 18\u201322). Detection of Distracted Driver using Convolutional Neural Network. Proceedings of the 2018 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, Utah, USA.","DOI":"10.1109\/CVPRW.2018.00150"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Petrovska, B., Zdravevski, E., Lameski, P., Corizzo, R., \u0160tajduhar, I., and Lerga, J. (2020). Deep learning for feature extraction in remote sensing: A case-study of aerial scene classification. Sensors, 20.","DOI":"10.3390\/s20143906"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Petrovska, B., Atanasova-Pacemska, T., Corizzo, R., Mignone, P., Lameski, P., and Zdravevski, E. (2020). Aerial scene classification through fine-tuning with adaptive learning rates and label smoothing. Appl. Sci., 10.","DOI":"10.3390\/app10175792"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Luo, Z., Li, J., Chen, C., and Piao, Y. (2020). When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on A Multitask Learning Framework. Remote Sens., 12.","DOI":"10.3390\/rs12203276"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Izadpanahkakhk, M., Razavi, S.M., Taghipour-Gorjikolaie, M., Zahiri, S.H., and Uncini, A. (2018). Deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning. Appl. Sci., 8.","DOI":"10.3390\/app8071210"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the Thirty-First Aaai Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the 16th IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_39","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/1\/1\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:48:15Z","timestamp":1760179695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,22]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["fi13010001"],"URL":"https:\/\/doi.org\/10.3390\/fi13010001","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,22]]}}}