RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality
TL;DR: This paper proposes a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings, which enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
Abstract: Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
Citations
More filters
Journal Articleβ’
TL;DR: Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
Abstract: The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
312Β citations
TL;DR: In this article, the authors proposed a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions.
Abstract: With smartphones being so ubiquitous, more connected and largely fitted with several types of sensors such as GPS, microphones, cameras, magnetometers, accelerometers, etc; there is an increasing opportunity in the development of smartphone-based sensor systems. In this article, we propose a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions. We also list the main advantages and limitations of the use of smartphone sensors systems in such fields.
36Β citations
TL;DR: In this article , the authors describe the body of knowledge in smartphone-based roughness assessment, report knowledge gaps and cast light on future research directions. But, they focus on practical factors that are expected to affect the accuracy and robustness of smartphonebased methods, including data collection speed, vehicle type, smartphone specifications and mounting configuration.
11Β citations
01 Mar 2018
8Β citations
TL;DR: The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance, however, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets.
Abstract: Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.
6Β citations
References
More filters
03 Dec 2012
TL;DR: This paper presents a system for human physical Activity Recognition using smartphone inertial sensors and proposes a novel hardware-friendly approach for multiclass classification that adapts the standard Support Vector Machine and exploits fixed-point arithmetic for computational cost reduction.
Abstract: Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.
802Β citations
"RoadCare: A Deep-learning Based App..." refers background in this paper
...We extract both raw as well as aggregated features from a single sample of 3 seconds and use them to train the SVM classifier....
[...]
...In this section we present the evaluation results for the SVM classifier in Section 4.1 and neural network based classification in Section 4.2....
[...]
...The evaluation of SVM with accelerometer features (ππππππ ) is shown in Section 4.1....
[...]
...We have evaluated the SVM classifier on our πΏπππ dataset for the set of features described in Section 3.2....
[...]
...Table 2 shows the classification accuracy for using SVM with βaccelerometer onlyβ features (ππππππ ) as well as with combined accelerometer and speed features (ππππππ+π ππππ ), for both linear and RBF (radial basis function) kernels....
[...]
TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixelβperfect accuracy.
Abstract: The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.
630Β citations
"RoadCare: A Deep-learning Based App..." refers background in this paper
...Other similar works [47, 48] use deep neural networks to ascertain the presence of pavement distress in varying conditions....
[...]
03 Oct 2018
TL;DR: The principles of morphological segmentation will be presented and illustrated by means of examples, starting from the simplest ones and introducing step by step more complex segmentation tools.
Abstract: This chapter presents the principles of morphological segmentation Segmentation is one of the key problems in image processing In fact, one should say segmentations because there exist as many techniques as there are specific situations An original method of segmentation based on the use of watershed lines has been developed in the framework of mathematical morphology The chapter describes some useful morphological tools for segmentation: gradient, top-hat transform, distance function, geodesic distance function, and geodesic reconstructions The gradient image is used in the watershed transformation, because the main criterion for the segmentation in many applications is the homogeneity of the gray values of the objects present in the image The problems encountered in the segmentation process will be best illustrated by presenting a complete and typical segmentation problem in the field of automated cytology The oversegmentation produced by direct construction of the watershed line is due to the fact that every regional minimum becomes the center of a catchment basin
480Β citations
"RoadCare: A Deep-learning Based App..." refers methods in this paper
...It first uses a watershed algorithm [4] to perform road segmentation, followed by Otsuβs binarization [39] and morphological thinning [5] to finally generate a topological skeleton of the pothole image....
[...]
TL;DR: This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images, and the results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.
450Β citations
"RoadCare: A Deep-learning Based App..." refers background in this paper
...Koch et al [23] uses geometric properties of a defect region, such as a pothole, and compares it with the texture of a non-defect region to detect if an anomaly is a pothole....
[...]
Bookβ’
08 Feb 1999
380Β citations
"RoadCare: A Deep-learning Based App..." refers methods in this paper
...We extract both raw as well as aggregated features from a single sample of 3 seconds and use them to train the SVM classifier....
[...]
...In this section we present the evaluation results for the SVM classifier in Section 4.1 and neural network based classification in Section 4.2....
[...]
...The evaluation of SVM with accelerometer features (ππππππ ) is shown in Section 4.1....
[...]
...We have evaluated the SVM classifier on our πΏπππ dataset for the set of features described in Section 3.2....
[...]
...Table 2 shows the classification accuracy for using SVM with βaccelerometer onlyβ features (ππππππ ) as well as with combined accelerometer and speed features (ππππππ+π ππππ ), for both linear and RBF (radial basis function) kernels....
[...]