A deep learning approach to automatic road surface monitoring and pothole detection

Varona, Braian Raúl; Monteserin, Ariel; Teyseyre, Alfredo Raúl

doi:10.1007/s00779-019-01234-z

Cited by 112 publications

(58 citation statements)

References 34 publications

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“…Both Perttunen et al [ 19 ] and Seraj et al [ 20 ] used a support vector machine (SVM) as the classification model. Silva et al [ 21 ], Varona et al [ 22 ], Lepine et al [ 23 ], and Basavaraju et al [ 24 ] analyzed and evaluated different classifiers. Borrowing ideas from the field of computer vision, Varona et al augmented datasets by stretching and shrinking the sequence of variation signals.…”

Section: Literature Reviewmentioning

confidence: 99%

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

Wang

et al. 2020

Sensors

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Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

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Section: Literature Reviewmentioning

confidence: 99%

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

Wang

et al. 2020

Sensors

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Section: Literature Reviewmentioning

confidence: 99%

“…The authors of [ 15 ] used a DL approach based on CNN (as in this paper) to perform the detection of road anomalies. The results provided in [ 15 ] confirmed the superior performance of the adoption of CNN in comparison to shallow machine learning algorithms, which prompted the authors of this paper to apply a CNN based approach as well. In comparison to the CNN approach used by the authors of [ 15 ], where the CNN was applied directly to the data collected from the accelerometers, this paper first transforms the accelerometer data in the spectral domain using the spectrogram defined in Section 3.4 , then the spectral representation is fed to a CNN (called CNN-SP in the rest of this paper).…”

Section: Literature Reviewmentioning

confidence: 99%

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

Baldini

Giuliani

Geib

2020

Sensors

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The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data. This approach is evaluated on an experimental dataset collected using 12 different vehicles driving for more than 40 km of road. The results show that the proposed approach outperforms significantly and across different sampling rates both the application of CNN to the original time domain representation and the application of shallow machine learning algorithms. The approach achieves an identification accuracy of 97.2%. The results presented in this paper are based on an extensive optimization both of the CNN algorithm and the spectrogram implementation in terms of window size, type of window, and overlapping ratio. The accurate detection of road anomalies/obstacles could be useful to road infrastructure managers to monitor the quality of the road surface and to improve the accurate positioning of autonomous vehicles because road anomalies/obstacles could be used as landmarks.

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“…Varona et al [7] explore how smart technologies can be employed to improve urban infrastructure. In their study, they propose to exploit accelerometer data that have been recorded using mobile phone devices to estimate road surface conditions.…”

Section: Accepted Articlesmentioning

confidence: 99%

“…In Sivanantham and Gopalakrishnan [6], the authors aim to identify peak energy usage in a smart grid to optimize energy demand and supply. An approach to identify road conditions based on the analysis of mobile phone accelerometer data recordings is presented in Varona et al [7]. The second group of papers focuses on how the mobility data gathered by individual citizens can be used to improve the quality of life of these citizens.…”

Section: Steps Aheadmentioning

confidence: 99%

Special issue on knowledge discovery and user modeling for smart cities

Armentano

Hopfgartner

Lykourentzou

et al. 2020

Pers Ubiquit Comput

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A deep learning approach to automatic road surface monitoring and pothole detection

Cited by 112 publications

References 34 publications

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

Special issue on knowledge discovery and user modeling for smart cities

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