Abstract
Object detection is challenging in the computer vision area and is crucial in autonomous driving systems. The largest number of traditional techniques or the use of deep learning are evaluated in the urban area, but in rural areas, there is little research carried out. The goal of this work is object detection in rural roads, this paper presents the use of deep learning frameworks used as You Only Look Once (YOLO) and another that belongs to the same category of one-stage is the well-known Single Shot Multi-Box Detector (SSD), in the state of the literature, produces excellent results in detecting objects in real-time. These models behave differently in network architecture, for this reason, we use images of rural roads with different environments to achieve an optimal balance between precision and precision in the detection of objects. Furthermore, as there is no dataset in these environments, we created our own data set to perform the experiments due to the difficulty of this problem. The result of both detectors has produced acceptable results under certain conditions like lighting conditions, viewing perspectives, partial occlusion of the object.
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Acknowledgment
This work is supported by the Artificial Intelligence Laboratory of the Technical University of Loja, Ecuador. University Institute of Automobile Research (INSIA) from Spain.
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Barba-Guaman, L., Naranjo, J.E., Ortiz, A., Gonzalez, J.G.P. (2021). Object Detection in Rural Roads Through SSD and YOLO Framework. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_17
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