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Author |
Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia |
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2023 |
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Dense extreme inception network for edge detection. Pattern Recognition |
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Vol. 139 |
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cidis @ cidis @ |
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216 |
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Abel Rubio, Wilton Agila, Leandro González & Jonathan Aviles-Cedeno |
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Distributed Intelligence in Autonomous PEM Fuel Cell Control. |
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2023 |
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Energies 2023 |
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Vol. 16 |
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Issue 12 |
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cidis @ cidis @ |
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217 |
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Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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2023 |
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In journal Artificial Intelligence in Agriculture |
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Vol. 9 |
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pp. 89-99 |
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cidis @ cidis @ |
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223 |
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Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
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2020 |
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In Sensors |
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Vol. 2020-August |
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16 |
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pp. 1-23 |
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Keywords |
object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities |
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Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images o ers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with di erent types of semantic segmentation networks and using the same evaluation metrics is
also included. |
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English |
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English |
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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
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133 |
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