Um sistema embarcado para detecção de sirenes utilizando aprendizado profundo
This work presents an embedded system development for siren detection using deep learning. In urban areas, emergency vehicles sirens play a role of alerting drivers and pedestrians to an emergency situation that requires a quick response. However, the acoustic insulation of vehicles and urban noise...
| Autor principal: | Barreto, Lucas Luis de Souza |
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| Grau: | Trabalho de Conclusão de Curso |
| Idioma: | por |
| Publicado em: |
Brasil
2024
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| Assuntos: | |
| Acesso em linha: |
http://riu.ufam.edu.br/handle/prefix/7543 |
| Resumo: |
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This work presents an embedded system development for siren detection using deep learning. In urban areas, emergency vehicles sirens play a role of alerting drivers and pedestrians to an emergency situation that requires a quick response. However, the acoustic insulation of vehicles and urban noise often make it difficult for sirens to be heard. This work contributes to solving this problem by using a microcontroller, audio processing techniques, and convolutional neural networks to detect sirens and take action to assist these emergency vehicles. The system achieved good performance metrics, most above 95%. This result demonstrates the feasibility of applying deep learning in embedded systems to improve traffic safety and efficiency. |