2018
DOI: 10.1109/tits.2017.2773084
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Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings—Addressing <italic>Who’s Who</italic>

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Cited by 63 publications
(35 citation statements)
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“…With 90% data set to be training set, our method achieves 11% better than the best of other methods. (2) In metal bump identification, the result of our method and the best performance of other methods is very close. Our method only takes a 0.02 advantage in F1 score.…”
Section: Experimental Settingsmentioning
confidence: 57%
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“…With 90% data set to be training set, our method achieves 11% better than the best of other methods. (2) In metal bump identification, the result of our method and the best performance of other methods is very close. Our method only takes a 0.02 advantage in F1 score.…”
Section: Experimental Settingsmentioning
confidence: 57%
“…Recently, a few studies first extracted different kinds of features [7][8][9][10][11][12] and then used classification models 9,[11][12][13] such as support vector machine, k-means, or decision tree. These methods suffer a few limitations: (1) they rarely filter out the impact of normal roads on model training; (2) the data between two anomalies in the same category may be similar in time domain, but none of them take this into consideration; and (3) they often use a fixed window length, leading to the possibility to slice anomalies' data.…”
Section: Introductionmentioning
confidence: 99%
“…Despite the noteworthy improvement of the Information and Communication Technologies (ICT), which is very well represented by the emerging field of the Internet of Things (IoT), it is still rare to find, in real contexts, monitoring systems based on ICT or IoT solutions that are able to detect and monitoring concealed distresses (e.g., bottom-up cracks). The current monitoring systems refer to mobile scanning technologies, such as those based on instrumented vehicles [9], unmanned aerial vehicles, airplanes, and satellites [10], ground penetrating radar [11], traffic speed deflectometer [12], smartphones' accelerometers [13], and non-nuclear density gauges [14]. The main drawback of the above-mentioned technologies refers to the fact that they are focused on the recognition of surface distresses only, or on the derivation of these latter from surface-related parameters (e.g., texture and regularity [15], noise propagation [16], sound absorption [17], and vibration [18]).…”
Section: State Of the Art About Technological Solutions Used To Detecmentioning
confidence: 99%
“…In RoadSense [129], Decision Tree (DT) was designed and compared with SVM and Naïve Bayes algorithm after feature extraction. In Pothole lab [130], a new SVM(Z) and Swarm indices were developed to compare with the four thresholds in [62], Nericell, Pothole Patrol, and PERT [119]. Backward feature elimination was used in [131] to select the optimal set of features for different classification models while in [132] the forward selection and backwards elimination process was performed showing better performance than existing approaches.…”
Section: Machine Learning Techniquesmentioning
confidence: 99%