Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM
|Course||Communication and Information System|
|Keywords||clutter suppression layer interface detection layer interface smoothing feature extraction support vector machine|
The maintenance of highway has been getting more and more important and labor-intensive because roadway network system is extending, and most of built roads are aging. As a fast, continuous, secure, nondestructive, real-time detection tool with high precision, GPR （ground penetrating radar） has been used in highway pavement distress detection. However, GPR is different from optical imaging equipment as it is not able to reflect the feature of object directly. Thus, during GPR is used to survey the quality of highway pavement, how to interpret the acquired GPR data as the quality status of highway pavement becomes the key issue.Using modern digital processing, signal detection and pattern recognition algorithm, highway pavement distress has been detected automatically in this project. The main techniques and detailed research work are list out as follows:1. Research on GPR original data preprocessing algorithm for clutter suppression, layer interface detection, layer interface smoothing, and ROI extraction.2. Research on feature extraction algorithm in time domain and wavelet domain. Extract three features from time domain: maximum amplitudes （MAXS）, mean absolute deviation （MAD） of amplitudes （MADS）, and cross-correlation of original signals amplitude （XCORRS）. And extract another three features from wavelet: summation of cross-correlation of synthesized signals at all level of wavelet (XCORRd123), magnitude of wavelet approximate coefficient a3 (MAXa3), and MAD of approximate coefficient a3 (MADa3).3. Research on pavement distress detection algorithm based on SVM. Basing on expert experiences training samples and testing samples are selected from GPR reflected signals in good and deteriorative pavement. Train SVM with six extracted features of training samples obtaining support vector network correspondingly, then input testing samples into SVM and implement feature classification determining whether there is distress in pavement or not.