[1] Road Traffic Injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries, Accessed April 21, 2025.
[2] Sinha, A., Aneesh, R., and Gopal, S. K., "Drowsiness detection system using deep learning," International Conference on Bio Signals, Images, and Instrumentation, 2021.
[3] پ. عمرانی, س. داودی, "بررسی تمایل رانندگان به استفاده از تلفن همراه در هنگام رانندگی در دو حالت تلفن در دست و هندزفری, " اولین کنفرانس ملی مهندسی عمران، توسعه هوشمند و سیستمهای پایدار, 1400.
[4] Doudou, M., Bouabdallah, A., and Berge-Cherfaoui, V., "Driver drowsiness measurement technologies: current research, market solutions, and challenges," International Journal of Intelligent Transportation Systems Research, Vol. 18, pp. 297-319, 2020.
[5] Saleem, A. A., Siddiqui, H. U. R., Raza, M. A., Rustam, F., Dudley, S., and Ashraf, I., "A systematic review of physiological signals based driver drowsiness detection systems," Cognitive Neurodynamics, Vol. 17, No. 5, pp. 1229–1259, 2023.
[6] Pal, A., Kar, S., and Bharti, M., "Algorithm for distracted driver detection and alert using deep learning," Optical Memory and Neural Networks, Vol. 30, pp. 257–265, 2021.
[7] Alsrehin, N. O., Gupta, M., Alsmadi, I., and Alrababah, S. A., "U2-net: a very deep convolutional neural network for detecting distracted drivers," Applied Sciences, Vol. 13, No. 21, p. 11898, 2023.
[8] Eraqi, H. M., Abouelnaga, Y., Saad, M. H., and Moustafa, M. N., "Driver distraction identification with an ensemble of convolutional neural networks," Journal of Advanced Transportation, Vol. 2019, p. 4125865, 2019.
[9] Xiao, W., Liu, H., Ma, Z., and Chen, W., "Attention-based deep neural network for driver behavior recognition," Future Generation Computer Systems, Vol. 132, pp. 152–161, 2022.
[10] Ma, Y. and Wang, Z., "Vit-dd: multi-task vision transformer for semi-supervised driver distraction detection," Intelligent Vehicles Symposium, pp. 417–423, 2024.
[11] Alom, M. Z. et al., "The history began from alexnet: a comprehensive survey on deep learning approaches," arXiv, 2018.
[12] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., "Rethinking the inception architecture for computer vision," IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826, 2016.
[13] He, K., Zhang, X., Ren, S., and Sun, J., "Deep residual learning for image recognition," IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016.
[14] Simonyan, K. and Zisserman, A., "Very deep convolutional networks for large-scale image recognition," arXiv, 2014.
[15] Dosovitskiy, A. et al., "An image is worth 16x16 words: transformers for image recognition at scale," arXiv, 2020.
[16] Zhang, Z. and Yang, Y., "A survey on multi-task learning," IEEE Transactions on Knowledge and Data Engineering, Vol. 34, No. 12, pp. 5586–5609, 2022.
[17] Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., and Lin, T.-Y., "Multi-task self-training for learning general representations," IEEE International Conference on Computer Vision, pp. 8856–8865, 2021.
[18] Mase, J. M., Chapman, P., Figueredo, G. P., and Torres, M. T., "A hybrid deep learning approach for driver distraction detection," International Conference on ICT Convergence, 2020.
[19] Graves, A. and Schmidhuber, J., "Framewise phoneme classification with bidirectional LSTM and other neural network architectures," Neural Networks, Vol. 18, pp. 602–610, 2005.
[20] Gao, H. and Liu, Y., "Improving real-time driver distraction detection via constrained attention mechanism," Engineering Applications of Artificial Intelligence, Vol. 128, p. 107408, 2024.
[21] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A., "Learning deep features for discriminative localization," IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929, 2016.
[22] Khan, T., Choi, G., and Lee, S., "Effnet-ca: an efficient driver distraction detection based on multiscale features extractions and channel attention mechanism," Sensors, Vol. 23, No. 8, p. 3835, 2023.
[23] Tan, M. and Le, Q., "Efficientnet: rethinking model scaling for convolutional neural networks," International Conference on Machine Learning, pp. 6105–6114, 2019.
[24] Mittal, H. and Verma, B., "Cat-capsnet: a convolutional and attention based capsule network to detect the driver's distraction," IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 9, pp. 9561–9570, 2023.
[25] LaLonde, R. and Bagci, U., "Capsules for object segmentation," arXiv, 2018.
[26] Duan, C., Liao, J., Ding, N., and Cao, L., "Enabling robust distracted driving performance across datasets with clip," International Conference on SMC-IoT, 2023.
[27] Radford, A. et al., "Learning transferable visual models from natural language supervision," International Conference on Machine Learning, pp. 8748–8763, 2021.
[28] Duan, C., Liu, Z., Xia, J., Zhang, M., Liao, J., and Cao, L., "Enhancing cross-dataset performance of distracted driving detection with score-softmax classifier," arXiv, 2023.
[29] Zhang, Z., Yang, L., and Lv, C., "Highly discriminative driver distraction detection method based on swin transformer," Vehicles, Vol. 6, No. 1, pp. 140–156, 2024.
[30] Liu, Z. et al., "Swin transformer: hierarchical vision transformer using shifted windows," IEEE International Conference on Computer Vision, pp. 10012–10022, 2021.
[31] Khellal, A., Boulahmar, M., Bahi, A., and Nemra, A., "Distracted driver detection using convolutional neural networks based segmentation model," International Conference on Electrical Engineering and Automatic Control, 2024.
[32] Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H., "Encoder-decoder with atrous separable convolution for semantic image segmentation," European Conference on Computer Vision, pp. 801–818, 2018.
[33] م. هاشمی، ع. میررشید، س. ع. بهشتی شیرازی، "تشخیص حواس پرتی و خواب آلودگی راننده از طریق روشهای مبتنی بر پردازش تصویر و یادگیری عمیق, " فناوری اطلاعات و ارتباطات انتظامی, 1399.
[34] ف. نساء، ع. ا. خواصی، "تشخیص حواس پرتی راننده مبتنی بر الگوریتمهای بینایی ماشین با استفاده از ویژگی های چشمی, " چهارمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات, 1397.
[35] م.ع. چاروسائی، ی. کبیری، ح.ر. چاروسائی، "طراحی رویکردهای یادگیری عمیق سیستم های تشخیص حواس پرتی راننده, " ششمین کنفرانس بین المللی مطالعات جهانی در علوم تکنولوژی و مهندسی, 1401.
[36] Tang, Z., Gao, Y., Karlinsky, L., Sattigeri, P., Feris, R., and Metaxas, D., "OnlineAugment: Online Data Augmentation with Less Domain Knowledge," European Conference on Computer Vision (ECCV), pp. 313–329, 2020.
[37] State Farm. "State Farm Distracted Driver Detection.", https://www.kaggle.com/c/state-farm-distracted-driver-detection/overview, Accessed April 21, 2025.
[38] Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K., "Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size," arXiv, 2016.
[39] Howard, A. et al., "Searching for mobilenetv3," IEEE International Conference on Computer Vision, pp. 1314–1324, 2019.
[40] Szegedy, C. et al., "Going deeper with convolutions," IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[41] Ma, N., Zhang, X., Zheng, H.-T., and Sun, J., "Shufflenet v2: practical guidelines for efficient cnn architecture design," European Conference on Computer Vision, pp. 116–131, 2018.
[42] Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q., "Densely connected convolutional networks," IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708, 2017.
[43] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S., "A convnet for the 2020s," IEEE Conference on Computer Vision and Pattern Recognition, pp. 11976–11986, 2022.
[44] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L., "Imagenet: a large-scale hierarchical image database," IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, 2009.
[45] Vaswani, A. et al., "Attention is all you need," Advances in Neural Information Processing Systems, Vol. 30, 2017.
[46] Chen, S., Ogawa, Y., Zhao, C., and Sekimoto, Y., "Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 195, pp. 129–152, 2023.
[47] Hu, J., Shen, L., and Sun, G., "Squeeze-and-excitation networks," IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141, 2018.
[48] Tajbakhsh, N. et al., "Convolutional neural networks for medical image analysis: full training or fine tuning?," IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1299–1312, 2016.
[49] Liu, Z. et al., "Kan: kolmogorov-arnold networks," arXiv, 2024.
[50] Hasan, Md. S., Alam, Md. N., Asad, Md. F.-A., Muhammad, N., and Tunç, C., "B-spline curve theory: an overview and applications in real life," Nonlinear Engineering, Vol. 13, p. 20240054, 2024.
[51] Alzubaidi, L. et al., "Review of deep learning: concepts, cnn architectures, challenges, applications, future directions," Journal of Big Data, Vol. 8, pp. 1–74, 2021.
[52] Qin, B., Zhang, B., and Qian, J., "ICK-PANet: A Multiscale Driver Distraction Detection Network Based on Attention and Pyramid Convolution," Vehicles, Vol. 8, No. 4, p. 83, 2026.
[53] Dokeroglu, T., Deniz, A., and Kiziloz, H. E., "A comprehensive survey on recent metaheuristics for feature selection," Neurocomputing, Vol. 494, pp. 269–296, 2022.
[54] Hinton, G., Vinyals, O., and Dean, J., "Distilling the knowledge in a neural network," arXiv, 2015.
