Robust Traffic Light Recognition Using Deep Learning and Object Tracking

Sungpyo Sagong; Ayoung Lee; Chaesong Park; Kyongsu Yi

doi:10.22680/kasa2025.17.1.006

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2025 Vol.17, Issue 1 Next Page

Robust Traffic Light Recognition Using Deep Learning and Object Tracking 딥러닝과 객체 추적을 이용한 강건한 신호등 인식 알고리즘 개발

31 March 2025. pp. 6-12

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Abstract

Recognition of traffic information is essential for high-level urban autonomous driving. Especially, the traffic light is the most important signal because it controls pedestrian and traffic flow. There are some methods such as V2X, image processing, and deep-learning to recognize traffic lights. This paper presents deep-learning based detection and tracking algorithm for traffic light signal recognition. We use the deep learning model YOLOv3 to classify traffic light signals in one stage for fast and accurate detection. Moreover, to make a robust deep learning model, we gather plenty of data from open-source data sets and vehicle front camera videos. Then, we combine the object tracking method using IOU to give reliability to continually detected signals to ignore sudden detection failures or misdetection. The experiment result from the approaching intersection scenario demonstrates the decent performance of traffic light recognition algorithm.

Keywords

Autonomous vehicle(자율주행)

Traffic light(신호등)

Object detection(객체 검출)

Object tracking(객체 추적)

Deep learning(딥러닝)

References

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Information

Publisher :Korean Auto-vehicle Safety Association
Publisher(Ko) :한국자동차모빌리티안전학회
Journal Title :Journal of Auto-vehicle Safety Association
Journal Title(Ko) :자동차안전학회지
Volume : 17
No :1
Pages :6-12
Received Date : 2023-10-04
Revised Date : 2024-08-06
Accepted Date : 2024-08-07
DOI :https://doi.org/10.22680/kasa2025.17.1.006

Journal of Auto-vehicle Safety Association ISSN:2005-9396(Print) 자동차안전학회지

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