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Ángel Morera, Á. S., A. Belén Moreno, Angel D. Sappa, & José F. Vélez. (2020). SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. In Sensors, Vol. 2020-August(16), pp. 1–23.
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 oers 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 dierent types of semantic segmentation networks and using the same evaluation metrics is
also included.
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Armin Mehri, P. B., Dario Carpio, and Angel D. Sappa. (2023). SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution. IEEE access, Vol. 11, 121457–121469.
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Santos V., Angel D. Sappa., & Oliveira M. & de la Escalera A. (2019). Special Issue on Autonomous Driving and Driver Assistance Systems. In Robotics and Autonomous Systems, 121.
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Victor Santos, Angel D. Sappa, & Miguel Oliveira. (2017). Special Issue on Autonomous Driving an Driver Assistance Systems. In Robotics and Autonomous Systems Journal, Vol. 91, pp. 208–209.
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Viñán-Ludeña M.S., D. C. L. M., Roberto Jacome Galarza, & Sinche Freire, J. (2020). Social media influence: a comprehensive review in general and in tourism domain. Smart Innovation, Systems and Technologies., 171, 2020, 25–35.
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Viñán-Ludeña, M. S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. (2020). Smart university: an architecture proposal for information management using open data for research projects. Advances in Intelligent Systems and Computing, 1137 AISC, 2020, 172–178.
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Rangnekar, A., Mulhollan, Z., Vodacek, A., Hoffman, M., Sappa, A. D., & Yu, J. et al. (2022). Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 389–397).
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Cristhian A. Aguilera, Xaver Soria, Angel D. Sappa, & Ricardo Toledo. (2017). RGBN Multispectral Images: a Novel Color Restoration Approach. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (Vol. 619, pp. 155–163).
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Velesaca, H. O., Suárez, P. L., Sappa, A. D., Carpio, D., Rivadeneira, R. E., & Sanchez, A. (2022). Review on Common Techniques for Urban Environment Video Analytics. In WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) (pp. 107–118).
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Dennis G. Romero, A. F. N., & Teodiano Freire B. (2014). Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes. Revista Iberoamericana de Automática e Informática industrial 00 (2014), Vol. 11, pp. 202–211.
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