Detection of Driver Dynamics with VGG16 Model

Aytekin, Alper; Mençik, Vasfiye

doi:10.2478/acss-2022-0009

Abstract

One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.

References

[1] O. Ursulescu, B. Ilie, and G. Simion, “Driver drowsiness detection based on eye analysis”, in 2018 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2018, pp. 1–4. https://doi.org/10.1109/ISETC.2018.8583852
Search in Google Scholar Back to article
[2] R. O. Mbouna, S. G. Kong, and M. G. Chun, “Visual analysis of eye state and head pose for driver alertness monitoring”, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1462–1469, Sep. 2013. https://doi.org/10.1109/TITS.2013.2262098
Search in Google Scholar Back to article
[3] S. Chinara,”Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal”, Journal of Neuroscience Methods, vol. 347, 2021, Art no. 108927. https://doi.org/10.1016/j.jneumeth.2020.10892732941920
Search in Google Scholar Back to article
[4] M. Dua, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection”, Neural Computing and Applications, vol. 33, no. 8, pp. 3155–3168, Jul. 2021. https://doi.org/10.1007/s00521-020-05209-7
Search in Google Scholar Back to article
[5] K. Dwivedi, K. Biswaranjan, and A. Sethi, “Drowsy driver detection using representation learning”, in Advance Computing Conference (IACC), Gurgaon, India, Mar. 2014, pp. 995–999. https://doi.org/10.1109/IAdCC.2014.6779459
Search in Google Scholar Back to article
[6] S. Park, F. Pan, S. Kang, and C. D. Yoo, “Driver drowsiness detection system based on feature representation learning using various deep networks”, in Asian Conference on Computer Vision, Taipei, Taiwan, Nov. 2016, pp. 154–164. https://doi.org/10.1007/978-3-319-54526-4_12
Search in Google Scholar Back to article
[7] S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD: A yawning detection dataset”, in Proceedings of the 5th ACM Multimedia Systems Conference, Singapore, Mar. 2014, pp. 24–28. https://doi.org/10.1145/2557642.2563678
Search in Google Scholar Back to article
[8] D. Cireşan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification”, arXiv: 1202.2745, Tech Rep. No. IDSIA-04-12, Feb. 2012. [Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/1202.2745.pdf
Search in Google Scholar Back to article
[9] B. Boser, J. D. Y. Le Cun, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network”, Advances in Neural Information Processing, vol. 2, 1989.
Search in Google Scholar Back to article
[10] K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv, preprint arXiv:1409.1556, 2014.
Search in Google Scholar Back to article
[11] J. Gwak, A. Hirao, and M. Shino, “An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing”, Appl. Sci., vol. 10, no. 8, Apr. 2020, Art no. 2890. https://doi.org/10.3390/app10082890
Search in Google Scholar Back to article
[12] S. Mehta, S. Dadhich, S. Gumber, and A. J. Bhatt, “Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio”, in Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Jaipur, India, Feb. 2019. https://doi.org/10.2139/ssrn.3356401
Search in Google Scholar Back to article
[13] Z. Kepesiova, J. Ciganek, and S. Kozak, “Driver drowsiness detection using convolutional neural networks”, in 2020 Cybernetics & Informatics (K&I), Velke Karlovice, Czech Republic, Mar. 2020, pp. 1–6. https://doi.org/10.1109/KI48306.2020.9039851
Search in Google Scholar Back to article
[14] R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, “Driver drowsiness detection model using convolutional neural networks techniques for android application”, in Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, Doha, Qatar, May 2020, pp. 2–5. https://doi.org/10.1109/ICIoT48696.2020.9089484
Search in Google Scholar Back to article

Detection of Driver Dynamics with VGG16 Model

Abstract

Paradigm

My account