Detecção de Anomalias em Pavimentos Rodoviários com Inteligência Artificial: Uma Visão Geral
Resumo
A deterioração da infraestrutura rodoviária pavimentada representa um desafio significativo para a segurança no transporte, a eficiência econômica e a gestão de recursos destinados à manutenção. Nesse cenário, o uso de técnicas de visão computacional e inteligência artificial (IA), especialmente o aprendizado profundo (deep learning), tem se destacado como uma alternativa promissora para automatizar a detecção de anomalias em pavimentos. Este artigo apresenta uma visão geral com o objetivo de identificar e categorizar o estado da arte em metodologias baseadas em IA aplicadas à detecção de danos em rodovias.
Palavras-chave:
Inteligência Artificial, Deep Learning, Detecção de Anomalias, Pavimentos Rodoviários, Visão Computacional
Referências
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Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., and Sekimoto, Y. (2021). Rdd2020: An annotated image dataset for automatic road damage detection using deep learning. Data in brief, 36:107133.
Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., and Sekimoto, Y. (2024). Rdd2022: A multi-national image dataset for automatic road damage detection. Geoscience Data Journal, 11(4):846–862.
Badrinarayanan, V., Kendall, A., and Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481–2495.
Buza, E., Akagic, A., Omanovic, S., and Hasic, H. (2017). Unsupervised method for detection of high severity distresses on asphalt pavements. In 2017 IEEE 14th International Scientific Conference on Informatics, pages 45–50. IEEE.
Chen, Q. and Fu, S. (2025). Continuous pavement crack detection using eca-enhanced instance segmentation of video images. Construction and Building Materials, 465:140247.
Chun, P.-j., Yamane, T., and Tsuzuki, Y. (2021). Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and GIS visualization. In Applied Sciences, volume 11, page 892. MDPI.
Deng, L., Zhao, X., Chu, H., and Xiang, C. (2022). Automatic inspection of cracks on pavement surfaces based on improved segmentation model. In International Conference on Smart Transportation and City Engineering (STCE 2022), volume 12460 of Proc. of SPIE, page 124602U.
Dong, H., Song, K., Wang, Y., Yan, Y., and Jiang, P. (2022). Automatic inspection and evaluation system for pavement distress. IEEE Transactions on Intelligent Transportation Systems, 23(8):12377–12387.
Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., and Gross, H.-M. (2017). How to get pavement distress detection ready for deep learning? a systematic approach. In International Joint Conference on Neural Networks (IJCNN), pages 2039–2047.
Freitas, G. T. d. M. and Nobre Júnior, E. F. (2020). Identificação de patologias em pavimentos rodoviários utilizando inteligência artificial. In Anais do 34º Congresso de Pesquisa e Ensino em Transportes, pages 831–834, 100% Digital. Associação Nacional de Pesquisa e Ensino em Transportes. Realizado de 16 a 21 de novembro de 2020.
Legramanti, G., Duarte, R. D., Gomes Junior, E. V., Dallagnol, S. L., Bisconsini, D. R., Felipetto, H. S., and Moraes, L. (2023). Clasificación supervisada de patologías en la superficie de los pavimentos de asfalto desde una aeronave pilotada remotamente (rpa). Revista ALCONPAT, 13(3):271–285. Epub 31-Mayo-2024.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). Ssd: Single shot multibox detector. In Leibe, B., Matas, J., Sebe, N., and Welling, M., editors, Computer Vision – ECCV 2016, pages 21–37, Cham. Springer International Publishing.
Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., and Omata, H. (2021). Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 36(1):47–60.
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33(12):1127–1141.
Mahmud, M. N., Osman, M. K., Ismail, A. P., Ahmad, F., Ahmad, K. A., Ibrahim, A., and Rabiani, A. H. (2024). Crack detection on asphalt road in malaysia using uav images and yolov4. In 2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE), pages 64–69.
Mello, F. B. (2024). Análise da aplicação da inteligência artificial, associada a dispositivos de coleta de imagens, como ferramentas para a detecção de patologias em vias públicas. Master’s thesis, Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis. Administração.
Moscoso Thompson, E., Ranieri, A., Biasotti, S., Chicchon, M., Sipiran, I., Pham, M.-K., Nguyen-Ho, T.-L., Nguyen, H.-D., and Tran, M.-T. (2022). Shrec 2022: Pothole and crack detection in the road pavement using images and rgb-d data. Computers & Graphics, 107:161–171.
Nunes-Ramos, V., Viera Trevisan, E., Pivoto Specht, L., da Silva Pereira, D., and Dotto Bueno, L. (2024). Distress manifestation in asphalt pavements: Comparison between local and unmanned aerial vehicle (uav) measurements. Anuário do Instituto de Geociências, 47.
Park, S.-S. and Nguyen, N.-N. (2025). Two-camera vision technique for measuring pothole area and depth. Measurement, 247:116809.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: towards real-time object detection with region proposal networks. In Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1, NIPS’15, page 91–99, Cambridge, MA, USA. MIT Press.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MIC- CAI 2015, pages 234–241, Cham. Springer International Publishing.
Shi, Y., Cui, L., Qi, Z., Meng, F., and Chen, Z. (2016). Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17(12):3434–3445.
Stricker, R., Aganian, D., Sesselmann, M., Seichter, D., Engelhardt, M., Spielhofer, R., Hahn, M., Hautz, A., Debes, K., and Gross, H.-M. (2021). Road surface segmentation - pixel-perfect distress and object detection for road assessment. In International Conference on Automation Science and Engineering (CASE), pages 1–8.
Stricker, R., Eisenbach, M., Sesselmann, M., Debes, K., and Gross, H.-M. (2019). Improving visual road condition assessment by extensive experiments on the extended gaps dataset. In International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Wang, D., Zhang, A. A., Peng, Y., Wei, Y., Cheng, H., and Shang, J. (2025). Adaptive learning network for detecting pavement distresses in complex environments. Engineering Applications of Artificial Intelligence, 152:110784.
Widodo, H., Taufiqurrohman, H., Muis, A., Wijayanto, Y. N., Prihantoro, G., Dwiyanti, H., Cahya, Z., Widaryanto, A., and Nugroho, T. H. (2024). Experimental evaluation of pothole detection and its dimension estimation using yolov8 and depth camera for road surface analysis. In 2024 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), pages 339–344.
Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., and Ling, H. (2019). Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems.
Zhang, L., Yang, F., Zhang, Y. D., and Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 3708–3712. IEEE.
Zhu, J., Zhong, J., Ma, T., Huang, X., Zhang, W., and Zhou, Y. (2022). Pavement distress detection using convolutional neural networks with images captured via UAV. Automation in Construction, 133:103991.
Zou, Q., Cao, Y., Li, Q., Mao, Q., and Wang, S. (2012). Cracktree: Automatic crack detection from pavement images. Pattern Recognition Letters, 33(3):227–238.
Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., and Sekimoto, Y. (2021). Rdd2020: An annotated image dataset for automatic road damage detection using deep learning. Data in brief, 36:107133.
Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., and Sekimoto, Y. (2024). Rdd2022: A multi-national image dataset for automatic road damage detection. Geoscience Data Journal, 11(4):846–862.
Badrinarayanan, V., Kendall, A., and Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481–2495.
Buza, E., Akagic, A., Omanovic, S., and Hasic, H. (2017). Unsupervised method for detection of high severity distresses on asphalt pavements. In 2017 IEEE 14th International Scientific Conference on Informatics, pages 45–50. IEEE.
Chen, Q. and Fu, S. (2025). Continuous pavement crack detection using eca-enhanced instance segmentation of video images. Construction and Building Materials, 465:140247.
Chun, P.-j., Yamane, T., and Tsuzuki, Y. (2021). Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and GIS visualization. In Applied Sciences, volume 11, page 892. MDPI.
Deng, L., Zhao, X., Chu, H., and Xiang, C. (2022). Automatic inspection of cracks on pavement surfaces based on improved segmentation model. In International Conference on Smart Transportation and City Engineering (STCE 2022), volume 12460 of Proc. of SPIE, page 124602U.
Dong, H., Song, K., Wang, Y., Yan, Y., and Jiang, P. (2022). Automatic inspection and evaluation system for pavement distress. IEEE Transactions on Intelligent Transportation Systems, 23(8):12377–12387.
Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., and Gross, H.-M. (2017). How to get pavement distress detection ready for deep learning? a systematic approach. In International Joint Conference on Neural Networks (IJCNN), pages 2039–2047.
Freitas, G. T. d. M. and Nobre Júnior, E. F. (2020). Identificação de patologias em pavimentos rodoviários utilizando inteligência artificial. In Anais do 34º Congresso de Pesquisa e Ensino em Transportes, pages 831–834, 100% Digital. Associação Nacional de Pesquisa e Ensino em Transportes. Realizado de 16 a 21 de novembro de 2020.
Legramanti, G., Duarte, R. D., Gomes Junior, E. V., Dallagnol, S. L., Bisconsini, D. R., Felipetto, H. S., and Moraes, L. (2023). Clasificación supervisada de patologías en la superficie de los pavimentos de asfalto desde una aeronave pilotada remotamente (rpa). Revista ALCONPAT, 13(3):271–285. Epub 31-Mayo-2024.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). Ssd: Single shot multibox detector. In Leibe, B., Matas, J., Sebe, N., and Welling, M., editors, Computer Vision – ECCV 2016, pages 21–37, Cham. Springer International Publishing.
Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., and Omata, H. (2021). Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 36(1):47–60.
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33(12):1127–1141.
Mahmud, M. N., Osman, M. K., Ismail, A. P., Ahmad, F., Ahmad, K. A., Ibrahim, A., and Rabiani, A. H. (2024). Crack detection on asphalt road in malaysia using uav images and yolov4. In 2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE), pages 64–69.
Mello, F. B. (2024). Análise da aplicação da inteligência artificial, associada a dispositivos de coleta de imagens, como ferramentas para a detecção de patologias em vias públicas. Master’s thesis, Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis. Administração.
Moscoso Thompson, E., Ranieri, A., Biasotti, S., Chicchon, M., Sipiran, I., Pham, M.-K., Nguyen-Ho, T.-L., Nguyen, H.-D., and Tran, M.-T. (2022). Shrec 2022: Pothole and crack detection in the road pavement using images and rgb-d data. Computers & Graphics, 107:161–171.
Nunes-Ramos, V., Viera Trevisan, E., Pivoto Specht, L., da Silva Pereira, D., and Dotto Bueno, L. (2024). Distress manifestation in asphalt pavements: Comparison between local and unmanned aerial vehicle (uav) measurements. Anuário do Instituto de Geociências, 47.
Park, S.-S. and Nguyen, N.-N. (2025). Two-camera vision technique for measuring pothole area and depth. Measurement, 247:116809.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: towards real-time object detection with region proposal networks. In Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1, NIPS’15, page 91–99, Cambridge, MA, USA. MIT Press.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MIC- CAI 2015, pages 234–241, Cham. Springer International Publishing.
Shi, Y., Cui, L., Qi, Z., Meng, F., and Chen, Z. (2016). Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17(12):3434–3445.
Stricker, R., Aganian, D., Sesselmann, M., Seichter, D., Engelhardt, M., Spielhofer, R., Hahn, M., Hautz, A., Debes, K., and Gross, H.-M. (2021). Road surface segmentation - pixel-perfect distress and object detection for road assessment. In International Conference on Automation Science and Engineering (CASE), pages 1–8.
Stricker, R., Eisenbach, M., Sesselmann, M., Debes, K., and Gross, H.-M. (2019). Improving visual road condition assessment by extensive experiments on the extended gaps dataset. In International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Wang, D., Zhang, A. A., Peng, Y., Wei, Y., Cheng, H., and Shang, J. (2025). Adaptive learning network for detecting pavement distresses in complex environments. Engineering Applications of Artificial Intelligence, 152:110784.
Widodo, H., Taufiqurrohman, H., Muis, A., Wijayanto, Y. N., Prihantoro, G., Dwiyanti, H., Cahya, Z., Widaryanto, A., and Nugroho, T. H. (2024). Experimental evaluation of pothole detection and its dimension estimation using yolov8 and depth camera for road surface analysis. In 2024 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), pages 339–344.
Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., and Ling, H. (2019). Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems.
Zhang, L., Yang, F., Zhang, Y. D., and Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 3708–3712. IEEE.
Zhu, J., Zhong, J., Ma, T., Huang, X., Zhang, W., and Zhou, Y. (2022). Pavement distress detection using convolutional neural networks with images captured via UAV. Automation in Construction, 133:103991.
Zou, Q., Cao, Y., Li, Q., Mao, Q., and Wang, S. (2012). Cracktree: Automatic crack detection from pavement images. Pattern Recognition Letters, 33(3):227–238.
Publicado
04/12/2025
Como Citar
BITTENCOURT, Pedro Martins; OLIVEIRA, Sávio Salvarino Teles de; PEREIRA, Lucas Araújo; SOARES, Anderson da Silva.
Detecção de Anomalias em Pavimentos Rodoviários com Inteligência Artificial: Uma Visão Geral. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 13. , 2025, Luziânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 282-291.
DOI: https://doi.org/10.5753/erigo.2025.17149.
