De Olho na Segurança: Como o Safe-Drive Detecta Distrações e Comportamentos Inseguros de Motoristas
Resumo
A segurança no trânsito urbano é um dos principais desafios enfrentados por cidades inteligentes, especialmente diante do crescente número de acidentes causados por distrações ao volante. Identificar e mitigar comportamentos distraídos e inseguros de motoristas em tempo real ainda representa um problema em aberto, devido à complexidade das cenas e às limitações das soluções existentes. Para enfrentar esse cenário, este trabalho propõe o SafeDrive, uma solução híbrida de visão computacional baseada em duas arquiteturas convolucionais do YOLO: uma dedicada à detecção de comportamentos distraídos e outra voltada à segmentação do uso do cinto de segurança. Quando comparado com outros trabalhos da literatura, o Safe-Drive mostrou alta taxa de acertos com uma redução no tempo de inferência de 52% no pior caso. Esses resultados destacam o Safe-Drive como uma solução eficaz e escalável na detecção de comportamentos distraídos e inseguros de motorista em tempo real.
Palavras-chave:
segurança no trânsito urbano, comportamentos distraídos, comportamentos inseguros, detecção em tempo real, YOLO
Referências
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Al-Mahbashi, M., Li, G., Peng, Y., Al-Soswa, M., and Debsi, A. (2025). Real-time distracted driving detection based on gm-yolov8 on embedded systems. Journal of Transportation Engineering, Part A: Systems, 151(3):04024126.
Bouhsissin, S., Sael, N., and Benabbou, F. (2023). Driver behavior classification: a systematic literature review. IEEE Access, 11:14128–14153.
Debsi, A., Ling, G., Al-Mahbashi, M., Al-Soswa, M., and Abdullah, A. (2024). Driver distraction and fatigue detection in images using me-yolov8 algorithm. IET Intelligent Transport Systems, 18(10):1910–1930.
Du, Y., Liu, X., Yi, Y., and Wei, K. (2023). Optimizing road safety: advancements in lightweight yolov8 models and ghostc2f design for real-time distracted driving detection. Sensors, 23(21):8844.
Elshamy, M. R., Emara, H. M., Shoaib, M. R., and Badawy, A.-H. A. (2024). P-yolov8: Efficient and accurate real-time detection of distracted driving. arXiv preprint arXiv:2410.15602.
Ferrante, G. S., Vasconcelos Nakamura, L. H., Sampaio, S., Filho, G. P. R., and Meneguette, R. I. (2024). Evaluating yolo architectures for detecting road killed endangered brazilian animals. Scientific Reports, 14(1):1353.
Fresta, M., Bellotti, F., Bochenko, I., Lazzaroni, L., Merlhiot, G., Tango, F., and Berta, R. (2025). Deep learning-based real-time driver cognitive distraction detection. IEEE Access.
Kshatri, S. S. and Rathore, Y. K. (2025). Real-time driver distraction detection using fast r-cnn algorithm.
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Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). SSD: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37. Springer.
Qu, F., Dang, N., Furht, B., and Nojoumian, M. (2024). Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques. Journal of Big Data, 11(1):32.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
Ribeiro Jr, A., da Costa, J. B., Rocha Filho, G. P., Villas, L. A., Guidoni, D. L., Sampaio, S., and Meneguette, R. I. (2023). Harmonic: Shapley values in market games for resource allocation in vehicular clouds. Ad Hoc Networks, 149:103224.
Rocha Filho, G. P., Meneguette, R. I., Neto, J. R. T., Valejo, A., Weigang, L., Ueyama, J., Pessin, G., and Villas, L. A. (2020). Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities. Ad Hoc Networks, 107:102265.
Sajid, F., Javed, A. R., Basharat, A., Kryvinska, N., Afzal, A., and Rizwan, M. (2021). An efficient deep learning framework for distracted driver detection. IEEE Access, 9:169270–169280.
Shen, Q., Zhang, L., Zhang, Y., Li, Y., Liu, S., and Xu, Y. (2024). Distracted driving behavior detection algorithm based on lightweight stardl-yolo. Electronics, 13(16):3216.
Shirole, V., Shahade, A. K., and Deshmukh, P. V. (2025). A comprehensive review on data-driven driver behaviour scoring in vehicles: technologies, challenges and future directions. Discover Artificial Intelligence, 5(1):26.
StateFarm (2016). State farm distracted driver detection. [link]. Acessado em 28 de outubro de 2024.
Wang, R., Wang, W., Gao, J., Lin, D., Yap, K.-H., and Li, B. (2024). Multifuser: Multimodal fusion transformer for enhanced driver action recognition. arXiv preprint arXiv:2408.01766.
World Health Organization (2022). Distracted driving. [link]. Acesso em: 22 mar. 2025.
Zhao, C., Zhang, B., Lian, J., He, J., Lin, T., and Zhang, X. (2011). Classification of driving postures by support vector machines. In 2011 Sixth International Conference on Image and Graphics, 926–930. IEEE.
Abouelnaga, Y., Eraqi, H. M., and Moustafa, M. N. (2017). Real-time distracted driver posture classification. arXiv preprint arXiv:1706.09498.
Al-Mahbashi, M., Li, G., Peng, Y., Al-Soswa, M., and Debsi, A. (2025). Real-time distracted driving detection based on gm-yolov8 on embedded systems. Journal of Transportation Engineering, Part A: Systems, 151(3):04024126.
Bouhsissin, S., Sael, N., and Benabbou, F. (2023). Driver behavior classification: a systematic literature review. IEEE Access, 11:14128–14153.
Debsi, A., Ling, G., Al-Mahbashi, M., Al-Soswa, M., and Abdullah, A. (2024). Driver distraction and fatigue detection in images using me-yolov8 algorithm. IET Intelligent Transport Systems, 18(10):1910–1930.
Du, Y., Liu, X., Yi, Y., and Wei, K. (2023). Optimizing road safety: advancements in lightweight yolov8 models and ghostc2f design for real-time distracted driving detection. Sensors, 23(21):8844.
Elshamy, M. R., Emara, H. M., Shoaib, M. R., and Badawy, A.-H. A. (2024). P-yolov8: Efficient and accurate real-time detection of distracted driving. arXiv preprint arXiv:2410.15602.
Ferrante, G. S., Vasconcelos Nakamura, L. H., Sampaio, S., Filho, G. P. R., and Meneguette, R. I. (2024). Evaluating yolo architectures for detecting road killed endangered brazilian animals. Scientific Reports, 14(1):1353.
Fresta, M., Bellotti, F., Bochenko, I., Lazzaroni, L., Merlhiot, G., Tango, F., and Berta, R. (2025). Deep learning-based real-time driver cognitive distraction detection. IEEE Access.
Kshatri, S. S. and Rathore, Y. K. (2025). Real-time driver distraction detection using fast r-cnn algorithm.
Kumar, K. S. and Raja, S. P. (2025). A comprehensive review of computer vision technology in driver monitoring system. Data Science & Exploration in Artificial Intelligence, 519–525.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). SSD: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37. Springer.
Qu, F., Dang, N., Furht, B., and Nojoumian, M. (2024). Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques. Journal of Big Data, 11(1):32.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
Ribeiro Jr, A., da Costa, J. B., Rocha Filho, G. P., Villas, L. A., Guidoni, D. L., Sampaio, S., and Meneguette, R. I. (2023). Harmonic: Shapley values in market games for resource allocation in vehicular clouds. Ad Hoc Networks, 149:103224.
Rocha Filho, G. P., Meneguette, R. I., Neto, J. R. T., Valejo, A., Weigang, L., Ueyama, J., Pessin, G., and Villas, L. A. (2020). Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities. Ad Hoc Networks, 107:102265.
Sajid, F., Javed, A. R., Basharat, A., Kryvinska, N., Afzal, A., and Rizwan, M. (2021). An efficient deep learning framework for distracted driver detection. IEEE Access, 9:169270–169280.
Shen, Q., Zhang, L., Zhang, Y., Li, Y., Liu, S., and Xu, Y. (2024). Distracted driving behavior detection algorithm based on lightweight stardl-yolo. Electronics, 13(16):3216.
Shirole, V., Shahade, A. K., and Deshmukh, P. V. (2025). A comprehensive review on data-driven driver behaviour scoring in vehicles: technologies, challenges and future directions. Discover Artificial Intelligence, 5(1):26.
StateFarm (2016). State farm distracted driver detection. [link]. Acessado em 28 de outubro de 2024.
Wang, R., Wang, W., Gao, J., Lin, D., Yap, K.-H., and Li, B. (2024). Multifuser: Multimodal fusion transformer for enhanced driver action recognition. arXiv preprint arXiv:2408.01766.
World Health Organization (2022). Distracted driving. [link]. Acesso em: 22 mar. 2025.
Zhao, C., Zhang, B., Lian, J., He, J., Lin, T., and Zhang, X. (2011). Classification of driving postures by support vector machines. In 2011 Sixth International Conference on Image and Graphics, 926–930. IEEE.
Publicado
19/05/2025
Como Citar
ALBARELLO, Ângela Brigida; CANEDO, Edna Dias; GONÇALVES, Vinícius P.; MENDONÇA, Fábio L. L.; SILVA, Francisco Airton; MENEGUETTE, Rodolfo I.; ROCHA FILHO, Geraldo P..
De Olho na Segurança: Como o Safe-Drive Detecta Distrações e Comportamentos Inseguros de Motoristas. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 9. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 57-70.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2025.8262.
