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Title: :  Traffic Sign Detection using Deep Learning
PaperId: :  19305
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-19305
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Brijesh B SDayananda Sagar College of Engineering
H Vishwanath ReddyDayananda Sagar College of Engineering
Srujan Jayaram RaoDayananda Sagar College of Engineering
Neha GuptaDayananda Sagar College of Engineering
Prof Sahana M PDayananda Sagar College of Engineering

Abstract

Computer Science and Engineering
Deep Learning, Traffic Control Management, OpenCV, Computer Vision, YOLO, R-CNN, Neural Networks, Advanced Driver Assistance Systems (ADAS), Intelligent Autonomous Vehicles (IV)
For autonomous driving systems, classifying traffic signs is a crucial task. Traffic signs vary greatly in appearance depending on the nation, which makes it more difficult for classification systems to be successful. A larger collection of images should be used, or the classifier should be improved. Advanced Driver Assistance Systems (ADAS) and Intelligent Autonomous Vehicles (IV) are currently used to address the issue of traffic sign recognition. Due to the various and intricate situations, they are put in, it is a difficult real-world computer vision problem. Images are grouped into categories like highway signs, speed signs, danger signs, etc. after being categorised. b In this project, we propose to investigate the YOLO Architecture and its compatibility in order to solve this problem. The goal is to find and classify traffic signs in natural street scenes. The main challenge in this problem is recognising minute targets in a large and complex image background. Other object detection models, such as Fast RCNN and Faster R-CNN, has been used to solve this problem. The main disadvantage of such methods is their slowness - they are not real-time. Thus, the motivation for investigating YOLO for this task is speed - it is about 6 faster than faster R-CNN. In this paper, we also propose a novel The modified loss function for the YOLO model to improve its performance in traffic sign detection.

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IJARIIE Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. "Traffic Sign Detection using Deep Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 341-346
MLA Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. "Traffic Sign Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 341-346.
APA Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, & Prof Sahana M P. (2023). Traffic Sign Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 341-346.
Chicago Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. "Traffic Sign Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 341-346.
Oxford Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. 'Traffic Sign Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 341-346. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Traffic_Sign_Detection_using_Deep_Learning_ijariie19305.pdf (Accessed : 16 October 2024).
Harvard Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. (2023) 'Traffic Sign Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 341-346IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Traffic_Sign_Detection_using_Deep_Learning_ijariie19305.pdf (Accessed : 16 October 2024)
IEEE Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P, "Traffic Sign Detection using Deep Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 341-346, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Traffic_Sign_Detection_using_Deep_Learning_ijariie19305.pdf [Accessed : 16 October 2024].
Turabian Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. "Traffic Sign Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 (16 October 2024).
Vancouver Brijesh B S, H Vishwanath Reddy, Srujan Jayaram Rao, Neha Gupta, and Prof Sahana M P. Traffic Sign Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 16 October 2024]; 9(3) : 341-346. Available from: https://ijariie.com/AdminUploadPdf/Traffic_Sign_Detection_using_Deep_Learning_ijariie19305.pdf
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