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Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. |
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Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends |
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2021 |
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In Journal: Robotics and Autonomous Systems. (Vol. 144, Article number 103832) |
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cidis @ cidis @ |
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158 |
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Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. |
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Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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2021 |
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In Computers and Electronics in Agriculture Journal. (Vol. 187, Article number 106287) |
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cidis @ cidis @ |
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159 |
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Author |
Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa |
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Title |
A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. |
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2022 |
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Journal of Manufacturing Systems |
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Vol. 64 |
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pp 497 – 507 |
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yes |
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cidis @ cidis @ |
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184 |
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Author |
Pereira J., Mora M. & W. Agila |
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Title |
Qualitative Model to Maximize Shrimp Growth at Low Cost |
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2021 |
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5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
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cidis @ cidis @ |
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167 |
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Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
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Title |
LDC: Lightweight Dense CNN for Edge Detection. |
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2022 |
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IEEE Access journal |
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Vol. 10 |
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pp 68281 – 68290 |
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yes |
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cidis @ cidis @ |
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183 |
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Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |
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Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
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2023 |
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IEEE Access |
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Vol. 11 |
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pp. 11529 - 11540 |
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no |
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cidis @ cidis @ |
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207 |
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Author |
Boris Vintimilla, Jorge Vulgarin, Henry Velesaca |
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Title |
Deep Learning-based Human Height Estimation from a Stereo Vision System |
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Year |
2023 |
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accepted in IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 |
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cidis @ cidis @ |
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215 |
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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, Vol. 139, 109461 |
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Call Number |
cidis @ cidis @ |
Serial |
216 |
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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |
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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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Journal Article |
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Year |
2020 |
Publication |
Sensors 2020 |
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Vol. 2020-June |
Issue |
11 |
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pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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English |
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14248220 |
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Call Number |
cidis @ cidis @ |
Serial |
132 |
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| Permanent link to this record |
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Author |
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. |
Type |
Journal Article |
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Year |
2020 |
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In Sensors |
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Volume |
Vol. 2020-August |
Issue |
16 |
Pages |
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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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
133 |
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