Abstract
This research work is focused on the field of object detection through computer vision, focusing on a very popular topic nowadays, smart cities. Therefore, a proposal is presented, after evaluating the different systems that have been used in the literature, based on deep learning techniques together with the use of images to detect traffic density. The developed technique corresponds to a You Only Look Once (YOLO) algorithm, which allows using previously trained weights or training the weights with a personalized dataset, for traffic detection. In relation to the results obtained after the application of these techniques, it can be observed how, depending on the data set used for training, the algorithm obtains different detections.
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Notes
- 1.
- 2.
Images source: https://storage.googleapis.com/openimages/web/index.html,
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Repository source: https://github.com/AlexeyAB/darknet#pre-trained-models.
- 4.
COCOCO Dataset: https://cocodataset.org/#home.
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Acknowledgement
This work was supported by the Spanish Agencia Estatal de Investigación. Project Monitoring and tracking systems for the improvement of intelligent mobility and behavior analysis (SiMoMIAC). PID2019-108883RB-C21/AEI/10.13039/501100011033.
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Martínez, D.P., López-Batista, V.F., de Paz Santana, J.F., Moreno-García, M.N., García, F. (2023). Object Detection Through Computer Vision. In: de la Iglesia, D.H., de Paz Santana, J.F., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2022. Advances in Intelligent Systems and Computing, vol 1430. Springer, Cham. https://doi.org/10.1007/978-3-031-14859-0_11
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