Breve revisión sobre inventario automatizado de señalética con drones

  1. Satama Bermeo, Geovanny 1
  2. Caballero Martín, Daniel
  3. Affou, Hicham 1
  4. Ramos-Hernanz, Josean 1
  5. Aramendia, Iñigo 1
  6. Lopez Guede, Jose 1
  1. 1 Universidad del País Vasco (UPV/EHU)
Revista:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Año de publicación: 2024

Número: 45

Tipo: Artículo

DOI: 10.17979/JA-CEA.2024.45.10907 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Este artículo presenta una breve revisión sobre la generación automatizada de inventarios de señalización vial mediante drones y aprendizaje profundo, utilizando la metodología PRISMA. Se analizaron 30 artículos de bases de datos académicas como Google Scholar, Science Direct y Web of Science. Los estudios revisados destacan las ventajas del uso de drones para la captura de imágenes y datos Lidar, así como la aplicación de algoritmos de inteligencia artificial para el procesamiento y análisis de datos. La literatura muestra que estas tecnologías permiten una gestión más eficiente y precisa de la señalización vial, mejorando la seguridad y la planificación urbana. También se identifican desafíos y futuras líneas de investigación, como la integración de diferentes tipos de sensores y el desarrollo de modelos más robustos para la detección y clasificación de señalización.

Referencias bibliográficas

  • Antwi, R. B., Takyi, S., Karaer, A., Ozguven, E. E., Moses, R., Dulebenets, M. A., Sando, T., 2023. Detecting school zones on florida’s public roadways using aerial images and artificial intelligence (ai2). Transportation Research Record 2677 (9), 227–240. DOI: 10.1177/03611981231185771 DOI: https://doi.org/10.1177/03611981231185771
  • Balado, J., González, E., Arias, P., Castro, D., 2020. Novel approach to automatic traffic sign inventory based on mobile mapping system data and deep learning. Remote Sensing 12 (3), 442. DOI: 10.3390/rs12030442 DOI: https://doi.org/10.3390/rs12030442
  • Ghorbanzadeh, O., Meena, S. R., Blaschke, T., Aryal, J., 2019. UAV-based slope failure detection using deep-learning convolutional neural networks. Remote Sensing 11 (17), 2046. DOI: 10.3390/rs11172046 DOI: https://doi.org/10.3390/rs11172046
  • Gupta, A., Mhala, P., Mangal, M., Yadav, K., Sharma, S., 2024a. Traffic sign sensing: A deep learning approach for enhanced road safety. Research Square. DOI: 10.21203/rs.3.rs-3889986/v1 DOI: https://doi.org/10.21203/rs.3.rs-3889986/v1
  • Gupta, P., Ding, B., Guan, C., Ding, D., 2024b. Generative AI: A systematic review using topic modelling techniques. Data and Information Management 8 (2), 100066. DOI: 10.1016/j.dim.2024.100066 DOI: https://doi.org/10.1016/j.dim.2024.100066
  • Huang, L., Qiu, M., Xu, A., Sun, Y., Zhu, J., 2022. UAV imagery for automatic multi-element recognition and detection of road traffic elements. Aerospace 9 (4), 198. DOI: 10.3390/aerospace9040198 DOI: https://doi.org/10.3390/aerospace9040198
  • Javanmardi, M., Song, Z., Qi, X., 2021. A fusion approach to detect traffic signs using registered color images and noisy airborne lidar data. Applied Sciences 11 (1), 309. DOI: 10.3390/app11010309 DOI: https://doi.org/10.3390/app11010309
  • Khan, M. N., Sharma, M., Gupta, D., Mittal, M., 2020. Automatic detection and classification of road conditions using statistical model for autonomous driving. Journal of Computing and Information Technology 28 (4), 453–468. DOI: 10.20532/cit.2020.1005180 DOI: https://doi.org/10.20532/cit.2020.1005180
  • Miranda, A., Catalán, G., Altamirano, A., Zamorano-Elgueta, C., Cavieres, M., Guerra, J., Mola-Yudego, B., 2021. How much can we see from a UAV-mounted regular camera? remote sensing-based estimation of forest attributes in south american native forests. Remote Sensing 13 (11), 2151. DOI: 10.3390/rs13112151 DOI: https://doi.org/10.3390/rs13112151
  • Munawar, H. S., Ullah, F., Qayyum, S., Heravi, A., 2021. Application of deep learning on UAV-based aerial images for flood detection. Smart Cities 4 (3), 1220–1242. DOI: 10.3390/smartcities4030065 DOI: https://doi.org/10.3390/smartcities4030065
  • Musa, A., 2022. Multi-view traffic sign localization with high absolute accuracy in real-time at the edge, 155–167. DOI: 10.1145/3557915.3561020 DOI: https://doi.org/10.1145/3557915.3561020
  • Naranjo, M., Fuentes, D., Muelas, E., Díez, E., Ciruelo, L., Alonso, C., Abenza, E., Gómez-Espinosa, R., Luengo, I., 2023. Object detection-based system for traffic signs on drone-captured images. Drones 7 (2), 112. DOI: 10.3390/drones7020112 DOI: https://doi.org/10.3390/drones7020112
  • Pal, O. K., Shovon, M. S. H., Mridha, M. F., Shin, J., 2023. A comprehensive review of AI-enabled unmanned aerial vehicle: Trends, vision, and challenges. arXiv preprint arXiv:2310.16360. DOI: 10.48550/arXiv.2310.16360
  • Panagiotidis, D., Abdollahnejad, A., Surový, P., Chiteculo, V., 2016. Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing 38 (7), 2150–2170. DOI: 10.1080/01431161.2016.1264028 DOI: https://doi.org/10.1080/01431161.2016.1264028
  • Piralilou, S. T., Shahabi, H., Jarihani, B., Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Aryal, J., 2019. Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas. Remote Sensing 11 (21), 2575. DOI: 10.3390/rs11212575 DOI: https://doi.org/10.3390/rs11212575
  • Rana, H., Babu, G. L. S., 2022. Object-oriented approach for landslide mapping using wavelet transform coupled with machine learning: A case study of western ghats, india. Indian Geotechnical Journal 52 (3), 691–706. DOI: 10.1007/s40098-021-00587-8 DOI: https://doi.org/10.1007/s40098-021-00587-8
  • Safonova, A., Hamad, Y., Dmitriev, E., Georgiev, G., Trenkin, V., Georgieva, M., Dimitrov, S., Iliev, M., 2021. Individual tree crown delineation for the species classification and assessment of vital status of forest stands from UAV images. Drones 5 (3), 77. DOI: 10.3390/drones5030077 DOI: https://doi.org/10.3390/drones5030077
  • Samsonov, P., Hecht, B., Schöning, J., 2015. From automatic sign detection to space usage rules mining for autonomous driving. In: Proceedings of the Workshop on Experiencing Autonomous Vehicles: Crossing the Boundaries between a Drive and a Ride at ACM CHI.
  • Sung, C., Jeon, S., Myung, H., 2022. What if there was no revisit? large-scale graph-based SLAM with traffic sign detection in an HD map using LiDAR inertial odometry. Intelligent Service Robotics 15 (2), 161–170. DOI: 10.1007/s11370-021-00395-2 DOI: https://doi.org/10.1007/s11370-021-00395-2
  • van Geffen, F., Heim, B., Brieger, F., Geng, R., Shevtsova, I. A., Schulte, L., Stuenzi, S. M., Bernhardt, N., Troeva, E. I., Pestryakova, L. A., Zakharov, E. S., Pflug, B., Herzschuh, U., Kruse, S., 2022. Sidroforest: a comprehensive forest inventory of siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and sentinel-2 labeled image patches. Earth System Science Data 14, 4967–4994. DOI: 10.5194/essd-14-4967-2022 DOI: https://doi.org/10.5194/essd-14-4967-2022
  • Xiong, J., Guo, P., Wang, Y., Meng, X., Zhang, J., Qian, L., Yu, Z., 2023. Multi-agent deep reinforcement learning for task offloading in group distributed manufacturing systems. Engineering Applications of Artificial Intelligence 118, 105710. DOI: 10.1016/j.engappai.2022.105710 DOI: https://doi.org/10.1016/j.engappai.2022.105710
  • Yadav, V. S., Singh, A., Gunasekaran, A., Raut, R. D., Narkhede, B. E., 2022. A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustainable Production and Consumption 29, 685–704. DOI: 10.1016/j.spc.2021.11.019 DOI: https://doi.org/10.1016/j.spc.2021.11.019
  • Yang, L., Li, X., Xia, Y., Aneja, Y., 2023. Returns operations in omnichannel retailing with buy-online-and-return-to-store. Omega 119, 102874. DOI: 10.1016/j.omega.2023.102874 DOI: https://doi.org/10.1016/j.omega.2023.102874
  • Yavas, V., Ozkan-Ozen, Y. D., 2020. Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transportation Research Part E: Logistics and Transportation Review 135, 101864. DOI: 10.1016/j.tre.2020.101864 DOI: https://doi.org/10.1016/j.tre.2020.101864
  • Yin, Y., Zheng, P., Li, C., Wang, L., 2023. A state-of-the-art survey on augmented reality-assisted digital twin for futuristic human-centric industry transformation. Robotics and Computer–Integrated Manufacturing 81, 102515. DOI: 10.1016/j.rcim.2022.102515 DOI: https://doi.org/10.1016/j.rcim.2022.102515
  • You, C., Wen, C., Wang, C., Li, J., Habib, A., 2019. Joint 2-d–3-d traffic sign landmark data set for geo-localization using mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems 20 (7), 2550–2565. DOI: 10.1109/TITS.2018.2868168 DOI: https://doi.org/10.1109/TITS.2018.2868168
  • Yu, L., Zhang, C., Jiang, J., Yang, H., Shang, H., 2021. Reinforcement learning approach for resource allocation in humanitarian logistics. Expert Systems with Applications 173, 114663. DOI: 10.1016/j.eswa.2021.114663 DOI: https://doi.org/10.1016/j.eswa.2021.114663
  • Yue, G., Tailai, G., Dan, W., 2021. Multi-layered coding-based study on optimization algorithms for automobile production logistics scheduling. Technological Forecasting & Social Change 170, 120889. DOI: 10.1016/j.techfore.2021.120889 DOI: https://doi.org/10.1016/j.techfore.2021.120889
  • Zajac, S., Huber, S., 2021. Objectives and methods in multi-objective routing problems: A survey and classification scheme. European Journal of Operational Research 290, 1–25. DOI: 10.1016/j.ejor.2020.07.005 DOI: https://doi.org/10.1016/j.ejor.2020.07.005
  • Zhao, Y., 2023. An ambient media advertising order snatch system based on price stepping algorithm. Procedia Computer Science 228, 983–992. DOI: 10.1016/j.procs.2023.10.196 DOI: https://doi.org/10.1016/j.procs.2023.11.129
Fuente de los datos: Dialnet