Volume 21 No 3 (2023)
 Download PDF
Deep Learning Model for Vehicle Taillight Detection and Recognization in Autonomous Driving
Gajula Mounika , Vasundra S
Automated cars are a technical advancement in the automobile industry. Automated vehicle detection can be employed as a part of forward collision avoidance and mitigation systems. Vehicles in front are typically detectable by their taillights when driving in low light. Real-time detection and identification of taillights can aid in preventing traffic incidents that may result from a driver's disregard for them. In this paper we have proposed YoloV5 model, which detect car taillight in autonomous driving. The results showed that the model perform well on a variety of criteria with 50 training iterations, the model achieved accuracy of 92.36%.
Autonomous Driving, YOLO, Taillight, Deep Learning (DL), Vehicle Detection.
Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.