Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

Maeda, Kazuo; Takahashi, Sho; Ogawa, Takahiro; Haseyama, Miki

doi:10.1111/mice.12451

Cited by 38 publications

(30 citation statements)

References 72 publications

(126 reference statements)

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“…For pavement health assessments, several image-based methods (Bang, Park, Kim, & Kim, 2019;Gopalakrishnan, Khaitan, Choudhary, & Agrawal, 2017;H. Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;K. Maeda, Takahashi, Ogawa, & Haseyama, 2019;Tong, Gao, Sha, Hu, & Li, 2018; and vehicle noise measurement (Ambrosini, Gabrielli, Vesperini, Squartini, & Cattani, 2018) have been studied.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Jeong

Ditzler

2020

Computer aided Civil Eng

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Road roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehiclemounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI-International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration-free) vehicles and their responses measured by smartphones as road roughness sensors. A state-of-the-art deep learning algorithm (i.e., CNN-convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers' smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Jeong

Ditzler

2020

Computer aided Civil Eng

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“…Deep Learning neural systems, being the latest incarnation of machine learning and currently considered state of the art in the field, have been investigated and applied in different domains such as civil engineering [21][22][23], data clustering [24,25] and many other fields [10,[26][27][28][29][30][31][32][33]. These research activities led to significant improvements also in image segmentation [15,34].…”

Section: Literature Reviewmentioning

confidence: 99%

Shallow buried improvised explosive device detection via convolutional neural networks

Colreavy‐Donnelly

Caraffini

Kühn

et al. 2020

ICA

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The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.

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“…Xu et al used the RVFL networks for learning of spatio-temporal processes [61]. Maeda et al [31] used a convolutional coding-based deep RVFL neural network for distress classification of roads. Tian et al [53] used RVFL networks for recognition of intrusion signal in an optical fiber warning system.…”

Section: Related Workmentioning

confidence: 99%

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

2020

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Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.

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Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

Cited by 38 publications

References 72 publications

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Shallow buried improvised explosive device detection via convolutional neural networks

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

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