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Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa |
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Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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2020 |
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Sensors |
Abbreviated Journal |
SENS |
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20 |
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11 |
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3249 |
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stereo matching; deep learning; embedded GPU |
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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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MSIAU; 600.122 |
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Admin @ si @ AAN2020 |
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3428 |
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Author |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez |
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities |
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Journal Article |
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2020 |
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Sensors |
Abbreviated Journal |
SENS |
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20 |
Issue |
16 |
Pages |
4587 |
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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 offers 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 difficulty 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 different types of semantic segmentation networks and using the same evaluation metrics is also included. |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ MSM2020 |
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3452 |
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Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa |
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Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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2021 |
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Computers and Electronics in Agriculture |
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CEA |
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187 |
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106287 |
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This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ VSM2021 |
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3576 |
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Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution |
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Year |
2022 |
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Sensors |
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SENS |
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22 |
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6 |
Pages |
2254 |
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Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images |
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This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. |
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MSIAU; |
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Admin @ si @ RSV2022b |
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3688 |
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Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
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Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
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2022 |
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Journal of Archaeological Science |
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JArchSci |
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148 |
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105654 |
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This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
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December 2022 |
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MSIAU; MACO; 600.167 |
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Admin @ si @ BOL2022 |
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3753 |
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