Recognition of vehicle light signals for smart traffic lights
This paper explores the application of machine learning methods for recognizing automobile light signals to enhance smart traffic light systems. For vehicle detection in video footage, the Keras library was employed along with the RetinaNet neural network architecture [1]. The YOLOv8 architecture wa...
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| Format: | Article |
| Language: | English |
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Belarusian National Technical University
2025-04-01
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| Series: | Системный анализ и прикладная информатика |
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| Online Access: | https://sapi.bntu.by/jour/article/view/727 |
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| _version_ | 1849242604671074304 |
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| author | K. S. Kurochka D. V. Prokopenko K. A. Panarin |
| author_facet | K. S. Kurochka D. V. Prokopenko K. A. Panarin |
| author_sort | K. S. Kurochka |
| collection | DOAJ |
| description | This paper explores the application of machine learning methods for recognizing automobile light signals to enhance smart traffic light systems. For vehicle detection in video footage, the Keras library was employed along with the RetinaNet neural network architecture [1]. The YOLOv8 architecture was used for identifying the status of vehicle headlights and taillights. Data collection, annotation, and model training were conducted using the Roboflow platform. The research resulted in trained model weights capable of recognizing the state of front and rear lights on various vehicle types under different weather conditions. The paper proposes an adaptation of the YOLOv8-based neural network model for recognizing traffic light signals, which can be utilized for both static recognition in photographs and in real-time or video applications. |
| format | Article |
| id | doaj-art-9bfb308559984d14948b78eeec3bf5dd |
| institution | Kabale University |
| issn | 2309-4923 2414-0481 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Системный анализ и прикладная информатика |
| spelling | doaj-art-9bfb308559984d14948b78eeec3bf5dd2025-08-20T03:59:47ZengBelarusian National Technical UniversityСистемный анализ и прикладная информатика2309-49232414-04812025-04-0101273110.21122/2309-4923-2025-1-27-31515Recognition of vehicle light signals for smart traffic lightsK. S. Kurochka0D. V. Prokopenko1K. A. Panarin2Sukhoi State Technical University of GomelSukhoi State Technical University of GomelSukhoi State Technical University of GomelThis paper explores the application of machine learning methods for recognizing automobile light signals to enhance smart traffic light systems. For vehicle detection in video footage, the Keras library was employed along with the RetinaNet neural network architecture [1]. The YOLOv8 architecture was used for identifying the status of vehicle headlights and taillights. Data collection, annotation, and model training were conducted using the Roboflow platform. The research resulted in trained model weights capable of recognizing the state of front and rear lights on various vehicle types under different weather conditions. The paper proposes an adaptation of the YOLOv8-based neural network model for recognizing traffic light signals, which can be utilized for both static recognition in photographs and in real-time or video applications.https://sapi.bntu.by/jour/article/view/727smart traffic lightsneural networksimage processingrecognition taskmodel training |
| spellingShingle | K. S. Kurochka D. V. Prokopenko K. A. Panarin Recognition of vehicle light signals for smart traffic lights Системный анализ и прикладная информатика smart traffic lights neural networks image processing recognition task model training |
| title | Recognition of vehicle light signals for smart traffic lights |
| title_full | Recognition of vehicle light signals for smart traffic lights |
| title_fullStr | Recognition of vehicle light signals for smart traffic lights |
| title_full_unstemmed | Recognition of vehicle light signals for smart traffic lights |
| title_short | Recognition of vehicle light signals for smart traffic lights |
| title_sort | recognition of vehicle light signals for smart traffic lights |
| topic | smart traffic lights neural networks image processing recognition task model training |
| url | https://sapi.bntu.by/jour/article/view/727 |
| work_keys_str_mv | AT kskurochka recognitionofvehiclelightsignalsforsmarttrafficlights AT dvprokopenko recognitionofvehiclelightsignalsforsmarttrafficlights AT kapanarin recognitionofvehiclelightsignalsforsmarttrafficlights |