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Traffic light recognition using deep neural networks
- Source :
- ICCE
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.
- Subjects :
- 050210 logistics & transportation
Engineering
business.industry
05 social sciences
020207 software engineering
02 engineering and technology
Machine learning
computer.software_genre
Traffic signal
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
False positive paradox
Deep neural networks
Artificial intelligence
business
computer
Classifier (UML)
Urban environment
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE International Conference on Consumer Electronics (ICCE)
- Accession number :
- edsair.doi...........73ad7a053a4342389ccd57c7ad83a673