Ten lectures on wavelets
Machine Learning
Automatic pavement distress detection system
Information Sciences—Informatics and Computer Science: An International Journal
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Digital Signal Processing
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
ACM Computing Surveys (CSUR)
A diffusion framework for detection of moving vehicles
Digital Signal Processing
Introduction to Machine Learning
Introduction to Machine Learning
IEEE Transactions on Information Forensics and Security
A comparison of multi-resolution methods for detection and isolation of pavement distress
Expert Systems with Applications: An International Journal
Potholes Detection Based on SVM in the Pavement Distress Image
DCABES '10 Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Distributed road surface condition monitoring using mobile phones
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
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Road condition monitoring through real-time intelligent systems has become more and more significant due to heavy road transportation. Road conditions can be roughly divided into normal and anomaly segments. The number of former should be much larger than the latter for a useable road. Based on the nature of road condition monitoring, anomaly detection is applied, especially for pothole detection in this study, using accelerometer data of a riding car. Accelerometer data were first labeled and segmented, after which features were extracted by wavelet packet decomposition. A classification model was built using one-class support vector machine. For the classifier, the data of some normal segments were used to train the classifier and the left normal segments and all potholes were for the testing stage. The results demonstrate that all 21 potholes were detected reliably in this study. With low computing cost, the proposed approach is promising for real-time application.