Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images

Molina‐Cabello, Miguel A.; Luque‐Baena, Rafael Marcos; López‐Rubio, Ezequiel; Thurnhofer‐Hemsi, Karl

doi:10.3233/ica-180577

Cited by 61 publications

(40 citation statements)

References 44 publications

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“…Recently, deep learning (DL) has emerged as one of the key enabling artificial intelligence technologies providing e2e learning (Zhang et al, 2017b;Rafiei and Adeli, 2018;Xue and Li 2018). Beyond its original application, image recognition (Koziarski and Cyganek, 2017;Ortega-Zamorano et al, 2017), in recent years, it has been employed in a large variety of applications such as thermal infrared face identification (Wang and Bai 2018), big data time series forecasting (Torres et al, 2018), vehicle type detection (Molina-Cabello et al 2018), traffic network management (Hashemi and Abdelghany, 2018;Nabian and Meidani, 2018), damage detection in structures (Gao and Mosalam 2018;Cha et al, 2018), road damage detection (Maeda et al 2018), and medical diagnosis applications (Antoniades et al 2018). DL architectures are divided into discriminative, generative, and hybrid (Deng and Yu, 2014;Salakhutdinov, 2015;Schmidhuber, 2015).…”

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confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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“…In addition, because the developed adaptive local approximation method in this paper is based on the characteristics of SVM, such as the support hyperplanes, other classification algorithms cannot be directly employed to take place of SVM. Some other popular classification algorithms have been extensively used in practical engineering, such as neural network (Ahmadlou & Adeli, ; Koziarski & Cyganek, ; Molina‐Cabello, Luque‐Baena, López‐Rubio, & Thurnhofer‐Hemsi, ; Wang & Bai, ; Xue & Li, ), neural dynamic classification (Rafiei & Adeli, , ), and deep learning techniques (Gao & Mosalam, ; Hashemi & Abdelghany, ; Rafiei & Adeli, , ; Rafiei, Khushefati, Demirboga, & Adeli, ; Zhang et al., ; Ortega‐Zamorano, Jerez, Gómez, & Franco, ; Torres, Galicia, Troncoso, & Martínez‐Álvarez, ). The applications of these classification algorithms in SRA‐RI can be investigated.…”

Section: Discussionmentioning

confidence: 99%

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

Zhang

Xiao

Gao

et al. 2019

Computer aided Civil Eng

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This paper investigates structural reliability analysis with both random and interval variables, which is defined as a three‐classification problem and handled by support vector machine (SVM). First, it is determined that projection outlines on the limit‐state surface are crucial for describing separating hyperplanes of the three‐classification problem. Compared with the whole limit‐state surface, the region of projection outlines are much smaller. It will be beneficial to reduce the number of update points and the computational cost if SVM update concentrates on refining the approximate projection outlines. An adaptive local approximation method is developed to realize that the initial built SVM model is sequentially updated by adding new training samples located around the projection outlines. Using this method, the separating hyperplanes can be accurately and efficiently approximated by SVM. Finally, a new method is proposed to evaluate the failure probability interval based on Monte Carlo simulation and the refined SVM.

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“…Since 2006, deep learning methods have been applied widely in various fields such as autonomous driving, natural language processing, computer vision, and drug discovery to enhance the state of the art and the state of the practice in these fields. Specifically, deep convolutional nets have brought about breakthroughs in processing images, video, speech, and audio (LeCun et al, ; Molina‐Cabello, Luque‐Baena, López‐Rubio, & Thurnhofer‐Hemsi, ; Wang & Bai, ).…”

Section: Methodsmentioning

confidence: 99%

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

Chen

Leng

Labi

2019

Computer aided Civil Eng

105

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Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in‐service roads, AV‐dedicated road networks, and AV test tracks. However, recent AV‐related fatalities on in‐service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real‐world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network–long short‐term memory (CNN–LSTM) algorithm for self‐driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving‐environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.

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Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images

Cited by 61 publications

References 44 publications

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information

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