Feature Extraction for Crack Detection in Rain Conditions

The solution of the features selection problem is critical for robust detection of crack signals in noisy environment, varying from short-time impulses such as raindrops to the wide-band white Gaussian noise. In this paper, two novel feature selection methods were used to reduce an initial set of 90 features, 67 estimated in the time domain and 23 in the frequency domain, decreasing significantly the memory requirements and the computational complexity of a Radial-Basis-Function (RBF) cracks detector. The evaluation process is carried out in a database including of more than 6000 cracks, raindrops and simultaneous crack and raindrops signals. Additive white Gaussian noise is used to distort the real signals at ?20 to 20 dB Signal to Noise Ratio (SNR). The experimental results show that the number of features can be reduced to approximately 25, without affecting the classification rate of cracks and raindrops in the noisy signals, if the SNR is better than 0 dB. In noise-free environment a classification rate of 91% for a single crack/raindrop event is achieved using only five features. A different set of five features reaches a rate of 85% at 10 dB SNR.