Deep Learning Model for Vehicle Taillight Detection and Recognization in Autonomous Driving

Volume 21 No 3 (2023)

Gajula Mounika , Vasundra S

DOI: 10.48047/NQ.2023.21.3.NQ33020

Abstract

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%.

Keywords

Autonomous Driving, YOLO, Taillight, Deep Learning (DL), Vehicle Detection.

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