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Mostrando ítems 1-10 de 14
Artículo
On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data
(MDPI, 2021)
Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance ...
Artículo
Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods
(Elsevier, 2018-03-01)
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural ...
Artículo
Real-time gun detection in CCTV: An open problem
(Elsevier, 2020-12)
Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection ...
Artículo
Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems
(Elsevier, 2017)
This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their ...
Ponencia
Vision and Crowdsensing Technology for an Optimal Response in Security: Project results
(IEEE Computer Society, 2021)
This paper describes the progress and work carried out during the execution of the VICTORY project (Vision and Crowdsensing Technology for an Optimal Response in Security). It describes both its motivation and the ...
Artículo
Asynchronous dual-pipeline deep learning framework for online data stream classification
(IOS Press, 2020)
Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming ...
Artículo
Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
(Elsevier, 2017)
Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features ...
Artículo
Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model
(Mary Ann Liebert, 2020)
This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, ...
Ponencia
Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting
(Springer, 2019)
In this paper, we introduce a deep learning approach, based on feed-forward neural networks, for big data time series forecasting with arbitrary prediction horizons. We firstly propose a random search to tune the multiple ...
Artículo
Deep Learning Techniques to Improve the Performance of Olive Oil Classification
(Frontiers Editorial, 2020)
The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography ...