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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63635
Volume:
10 |
5 |
May 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science Engineering
Guru Nanak Institute of Technology
Computer Science Engineering
Guru Nanak Institute of Technology
Computer Science Engineering
Guru Nanak Institute of Technology
Computer Science Engineering
Guru Nanak Institute of Technology
To ensure a smooth and secure flow of traffic, road signs are essential. A major cause of road accidents is negligence in viewing the Traffic signboards and interpreting them incorrectly. The proposed system helps in recognizing the Traffic sign and sending a voice alert through the speaker to the driver so that he/ she may take necessary decisions. The proposed system is trained using Convolutional Neural Network (CNN) which helps in traffic sign image recognition and classification. A set of classes are defined and trained on a particular dataset to make it more accurate. The German Traffic Sign Benchmarks Dataset was used, which contains approximately 43 categories and 51,900 images of traffic signs. The accuracy of the execution is about 98.52 percent. Following the detection of the sign by the system, a voice alert is sent through the speaker which notifies the driver. The proposed system also contains a section where the vehicle driver is alerted about the traffic signs in the near proximity which helps them to be aware of what rules to follow on the route. The aim of this system is to ensure the safety of the vehicle’s driver, passengers, and pedestrians.
KEYWORDS
In this paper, our group proposes and designs a Traffic Sign Board Detection and Voice Alert System is implemented using Convolutional Neural Network. various models under the CNN heading were studied and the one with highest accuracy on the GTSRB dataset was implemented. The creation of different classes for each Traffic sign has helped in increasing the accuracy of the model. A voice message is sent after recognition of the sign which alerts the driver, thus helping him/her take appropriate decisions. This paper is a significant advancement in the field of driving as it would ease the job of the driver without compromising on the safety aspect. Also this system can easily be implemented without the need of much hardware thus increasing its reach.
1 Yadav, Shubham & Patwa, Anuj & Rane, Saiprasad & Narvekar, Chhaya. (2019). Indian Traffic Sign Board Recognition and Driver Alert System Using Machine Learning. International Journal of Applied Sciences and Smart Technologies. 1. 1-10. 10.24071/ijasst.v1i1.1843. 2 Anushree.A., S., Kumar, H., Iram, I., & Divyam, K. (2019). Automatic Signboard Detection System by the Vehicles. 3 S. Harini, V. Abhiram, R. Hegde, B. D. D. Samarth, S. A. Shreyas and K. H. Gowranga, "A smart driver alert system for vehicle traffic using image detection and recognition technique," 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2017, pp. 1540-1543, doi: 10.1109/RTEICT.2017.8256856 4 C. Wang, "Research and Application of Traffic Sign Detection and Recognition Based on Deep Learning," 2018 International Conference on Robots & Intelligent System (ICRIS), Changsha, China, 2018, pp. 150-152, doi: 10.1109/ICRIS.2018.00047. 5 M A Muchtar et al 2017 J. Phys.: Conf. Ser. 801 012010 6 Y. Yuan, Z. Xiong and Q. Wang, "VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection," in IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3423- 3434, July 2019, doi: 10.1109/TIP.2019.2896952. 7 S. Huang, H. Lin and C. Chang, "An in-car camera system for traffic sign detection and recognition," 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), Otsu, Japan, 2017, pp. 1-6, doi: 10.1109/IFSA-SCIS.2017.8023239. 8 Bi, Z., Yu, L., Gao, H. et al. Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios. Int. J. Mach. Learn. & Cyber. (2020) 9 Chuanwei Zhang et al., Study on Traffic Sign Recognition by Optimized Lenet-5 Algorithm, International Journal of Pattern Recognition and Artificial Intelligence, doi:0.1142/S0218001420550034 10 Han, C., Gao, G. & Zhang, Y. Real-time small traffic sign detection with revised faster- RCNN. Multimed Tools Appl 78, 13263–13278 (2019). https://doi.org/10.1007/s11042-018-6428-0
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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