Breve revisión sobre inventario automatizado de señalética con drones
Barra lateral del artículo
Contenido principal del artículo
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.
Palabras clave:
Detalles del artículo
Citas
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