Deep ResNet 18 and enhanced firefly optimization algorithm for on-road vehicle driver drowsiness detection
Abstract
The driver drowsiness detection (DDD) technology is based on vehicle safety, and this system prevents many accidents and deaths that occur due to driver drowsiness. As a result, it is monitored and detected when vehicle drivers become drowsy. The DDD method, which is aided by AlexNet and deep learning models, has limitations such as vanishing gradients and overfitting issues as the depth of the model increases. The enhanced firefly optimisation algorithm has solved the problem of lower optimisation exploration. The National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset’s input image contains individual groups of female and male drivers of various vehicles. The Min-max normalisation method is a general method for normalising data. The convolutional neural network (CNN) is used to extract features from input images and images classified by the neural network. ResNet 18 refers to the deepest of the convolutional neural network’s 18 layers. A network of pre-trained models can be used to classify the model classified by the 1000 image objects. The state-of-the-art Hierarchical Deep Drowsiness Detection (HDDD) model with Support Vector Machine (SVM) assistance has an effective high dimensional space. The CNN-EFF-ResNet 18 models have a high accuracy of 91.3%, while the HDDD method has a higher accuracy of 87.19% than the ensemble and Pyramid Multi-level Deep Belief (PMLDB) methods in DDD.
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1. Hu S, Zheng G. Driver drowsiness detection with eyelid related parameters by Support Vector Machine. Expert Systems with Applications 2009; 36(4): 7651–7658. doi: 10.1016/j.eswa.2008.09.030
2. Wang H, Zhang L, Yao L. Application of genetic algorithm based support vector machine in selection of new EEG rhythms for drowsiness detection. Expert Systems with Applications 2021; 171: 114634. doi: 10.1016/j.eswa.2021.114634
3. You F, Li X, Gong Y, et al. A real-time driving drowsiness detection algorithm with individual differences consideration. IEEE Access 2019; 7: 179396–179408. doi: 10.1109/ACCESS.2019.2958667
4. Quddus A, Zandi AS, Prest L, Comeau FJE. Using long short term memory and convolutional neural networks for driver drowsiness detection. Accident Analysis Prevention 2021; 156: 106107. doi: 10.1016/j.aap.2021.106107
5. Babaeian M, Francis KA, Dajani K, Mozumdar M. Real-time driver drowsiness detection using wavelet transform and ensemble logistic regression. International Journal of Intelligent Transportation Systems Research 2019; 17(3): 212–222. doi: 10.1007/s13177-019-0176-z
6. Kiashari SEH, Nahvi A, Bakhoda H, et al. Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator. Multimedia Tools and Applications 2020; 79(25): 17793–17815. doi: 10.1007/s11042-020-08696-x
7. Flores MJ, Armingol JM, de la Escalera A. Real-time warning system for driver drowsiness detection using visual information. Journal of Intelligent & Robotic Systems 2010; 59(2): 103–125. doi: 10.1007/s10846-009-9391-1
8. Guo JM, Markoni H. Driver drowsiness detection using hybrid convolutional neural network and long short-term memory. Multimedia Tools and Applications 2019; 78(20): 29059–29087. doi: 10.1007/s11042-018-6378-6
9. Khare SK, Bajaj V. Optimized tunable Q wavelet transform based drowsiness detection from electroencephalogram signals. IRBM 2022; 43(1): 13–21. doi: 10.1016/j.irbm.2020.07.005
10. Minhas AA, Jabbar S, Farhan M, Najam ul Islam M. Smart methodology for safe life on roads with active drivers based on real-time risk and behavioral monitoring. Journal of Ambient Intelligence and Humanized Computing 2019; 1–13. doi: 10.1007/s12652-019-01554-1
11. Dua M, Singla R, Raj S, Jangra A. Deep CNN models-based ensemble approach to driver drowsiness detection. Neural Computing and Applications 2021; 33(8): 3155–3168. doi: 10.1007/s00521-020-05209-7
12. Moujahid A, Dornaika F, Arganda-Carreras I, Reta J. Efficient and compact face descriptor for driver drowsiness detection. Expert Systems with Applications 2021; 168: 114334. doi: 10.1016/j.eswa.2020.114334
13. Wijnands JS, Thompson J, Nice KA, et al. Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks. Neural Computing and Applications 2020; 32(13): 9731–9743. doi: 10.1007/s00521-019-04506-0
14. Jamshidi S, Azmi R, Sharghi M, Soryani M. Hierarchical deep neural networks to detect driver drowsiness. Multimedia Tools and Applications 2021; 80(10): 16045–16058. doi: 10.1007/s11042-021-10542-7
15. Shahverdy M, Fathy M, Berangi R, Sabokrou M. Driver behavior detection and classification using deep convolutional neural networks. Expert Systems with Applications 2020; 149: 113240. doi: 10.1016/j.eswa.2020.113240
16. Savaş BK, Becerikli Y. Real time driver fatigue detection system based on multi-task ConNN. IEEE Access 2020; 8: 12491–12498. doi: 10.1109/ACCESS.2020.2963960
17. Yasaka K, Akai H, Kunimatsu A, et al. Deep learning with convolutional neural network in radiology. Japanese Journal of Radiology 2018; 36(4): 257–272. doi: 10.1007/s11604-018-0726-3
18. Miguel LFF, Lopez RH, Miguel LFF. Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm. Advances in Engineering Software 2013; 56: 23–37. doi: 10.1016/j.advengsoft.2012.11.006
19. Ahuja S, Panigrahi BK, Dey N, et al. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Applied Intelligence 2021; 51(1): 571–585. doi: 10.1007/s10489-020-01826-w
20. Turkoglu M, Hanbay D, Sengur A. Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. Journal of Ambient Intelligence and Humanized Computing 2022; 13: 3335–3345. doi: 10.1007/s12652-019-01591-w.
21. Rangayya, Virupakshappa, Patil N. Improved face recognition method using SVM-MRF with KTBD based KCM segmentation approach. International Journal of System Assurance Engineering and Management 2022. doi: 10.1007/s13198-021-01483-3
DOI: https://doi.org/10.32629/jai.v7i3.975
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