A Deep Neural Inferencing Approach of Assistive Philippine Traffic Light Recognition: An Augmented Transfer Learning Approach
| Autor: | Aimee G. Acoba, Nenita D. Guerrero, Christopher Franco Cunanan, Cherry D. Casuat, Ma. Ian P. Delos Trinos |
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| Rok vydání: | 2021 |
| Předmět: |
Identification methods
Computer science business.industry Computational intelligence 02 engineering and technology Machine learning computer.software_genre Identification (information) Traffic signal 020204 information systems 0202 electrical engineering electronic engineering information engineering Recognition system 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer |
| Zdroj: | 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). |
| DOI: | 10.1109/iccike51210.2021.9410683 |
| Popis: | Study on the identification of traffic signals plays an important role not only for intelligent cars but also for traditional cars and their drivers. Various identification methods have been suggested over the years for traffic light identification, unfortunately not in the Philippine environments. In this study, the proponents developed a traffic light recognition system that could be used in crossroads/crosswalks in the Philippines for traffic light recognition. The researchers trained and tested the dataset using Yolo v3 and resulted in a mean average precision (mAP) of 0.88. The system has been tested by traffic lights in the Philippine environment, which has a good result in terms of traffic light detection and recognition. |
| Databáze: | OpenAIRE |
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