1
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O. BOX 16273, Al-Kharj, ZIP 3963, Saudi Arabia.
(m.assiri@psau.edu.sa)
Abstract :
Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers. Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.
Keywords :
Traffic Sign Detection; Automatic Driving; Intelligent Transport System; Neutrosophic Rule; Golden Jackal Optimization
References :
[1] Kaplan Berkaya, S.; Gunduz, H.; Ozsen, O.; Akinlar, C.; Gunal, S. On Circular Traffic Sign Detection and Recognition. Expert Syst. Appl. 2016, 48, 67–75.
[2] He, X.; Dai, B. A New Traffic Signs Classification Approach Based on Local and Global Features Extraction. In Proceedings of the 2016 6th International Conference on Information Communication and Management (ICICM), Hatfield, UK, 29–31 October 2016; pp. 121–125.
[3] X. Xu, J. Jin, S. Zhang, L. Zhang, S. Pu, and Z. Chen, ‘‘Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry,’’ Future Gener. Comput. Syst., vol. 94, pp. 381–391, May 2019.
[4] Y. Yang and F. Wu, ‘‘Real-time traffic sign detection via color probability model and integral channel features,’’ in Proc. Chin. Conf. Pattern Recognit. (CCPR), CCIS, vol. 484, 2014, pp. 545–554.
[5] Rajendran, S.P.; Shine, L.; Pradeep, R.; Vijayaraghavan, S. Real-Time Traffic Sign Recognition Using YOLOv3 Based Detector. In Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 6–8 July 2019; pp. 1–7.
[6] H. S. Lee and K. Kim, ‘‘Simultaneous traffic sign detection and boundary estimation using convolutional neural network,’’ IEEE Trans. Intell. Transp. Syst., vol. 19, no. 5, pp. 1652–1663, May 2018.
[7] T. Yang, X. Long, A. K. Sangaiah, Z. Zheng, and C. Tong, ‘‘Deep detection network for real-life traffic sign in vehicular networks,’’ Comput. Netw., vol. 136, pp. 95–104, May 2018.
[8] Y. Yuan, Z. Xiong, and Q. Wang, ‘‘VSSA-NET: Vertical spatial sequence attention network for traffic sign detection,’’ IEEE Trans. Image Process., vol. 28, no. 7, pp. 3423–3434, Jul. 2019.
[9] L. Yu, X. Xia, and K. Zhou, ‘‘Traffic sign detection based on visual cosaliency in complex scenes,’’ Appl. Intell., vol. 49, no. 2, pp. 764–790, Feb. 2019.
[10] H. Luo, Y. Yang, B. Tong, F. Wu, and B. Fan, ‘‘Traffic sign recognition using a multi-task convolutional neural network,’’ IEEE Trans. Intell. Transp. Syst., vol. 19, no. 4, pp. 1100–1111, Apr. 2018.
[11] Gomathy, V., Jayasankar, T., Rajaram, M., Devi, E.A. and Priyadharshini, S., 2022. Optimal neutrosophic rules based feature extraction for data classification using deep learning model. In Soft Computing for Data Analytics, Classification Model, and Control (pp. 57-79). Cham: Springer International Publishing.
[12] Mirza, O.M. and Samak, A.H., 2024. Neutrosophic Fuzzy Logic-Based Hybrid CNN-LSTM for Accurate Chest X-ray Classification in COVID-19 Prediction. Sciences, 18(1), p.14.
[13] Keerthi, S. and Santhi, P., 2023. Precise multi-class classification of brain tumor via optimization based relevance vector machine. Intelligent Automation and Soft Computing, 36(1), pp.1173-1188.
[14] Essameldin, R., Ismail, A.A. and Darwish, S.M., 2022. Quantifying Opinion Strength: A Neutrosophic Inference System for Smart Sentiment Analysis of Social Media Network. Applied Sciences, 12(15), p.7697.
[15] Hefny, H.A. and Amer, N.S., 2024. Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS). International Journal of Computational Intelligence Systems, 17(1), pp.1-23.
[16] Darwish, S.M., Salah, M.A. and Elzoghabi, A.A., 2023. Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired People. Applied Sciences, 13(4), p.2150.
[17] Chauhan, S.S. and Kumar, D., 2023. Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier. SN Computer Science, 4(5), p.686.
[18] Rashed, B.M. and Popescu, N., 2024. Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction. Mathematics, 12(5), p.633
[19] Agwa, A.M., Alanazi, T.I., Kraiem, H., Touti, E., Alanazi, A. and Alanazi, D.K., 2023. MPPT of PEM Fuel Cell Using PI-PD Controller Based on Golden Jackal Optimization Algorithm. Biomimetics, 8(5), p.426.
[20] Basha, S.H., Abdalla, A.S. and Hassanien, A.E., 2016, December. Gnrcs: hybrid classification system based on neutrosophic logic and genetic algorithm. In 2016 12th International Computer Engineering Conference (ICENCO) (pp. 53-58). IEEE.
[21] Basha, S.H., Abdalla, A. and Hassanien, A.E., 2021. Neutrosophic rule-based classification system and its medical applications. In Big Data in Psychiatry# x0026; Neurology (pp. 119-135). Academic Press.
Triki, N., Karray, M. and Ksantini, M., 2023. A real-time traffic sign recognition method using a new attention-based deep convolutional neural network for smart vehicles. Applied Sciences, 13(8),
| Style | # |
|---|---|
| MLA | Mohammed Assiri. "Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection." International Journal of Neutrosophic Science, Vol. 23, No. 4, 2024 ,PP. 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |
| APA | Mohammed Assiri. (2024). Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection. Journal of International Journal of Neutrosophic Science, 23 ( 4 ), 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |
| Chicago | Mohammed Assiri. "Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection." Journal of International Journal of Neutrosophic Science, 23 no. 4 (2024): 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |
| Harvard | Mohammed Assiri. (2024). Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection. Journal of International Journal of Neutrosophic Science, 23 ( 4 ), 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |
| Vancouver | Mohammed Assiri. Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection. Journal of International Journal of Neutrosophic Science, (2024); 23 ( 4 ): 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |
| IEEE | Mohammed Assiri, Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection, Journal of International Journal of Neutrosophic Science, Vol. 23 , No. 4 , (2024) : 29-40 (Doi : https://doi.org/10.54216/IJNS.230403) |