GPR Moisture Damage Recognition via Wavelet CNN

The patent replaces the mechanical human analysis process with an automated deep learning system. A convolutional neural network (CNN) model is trained to automatically interpret GPR signals and identify moisture damage, substituting manual expert inspection with an automated computational system that maintains high detection accuracy while dramatically improving processing efficiency

The system enables self-service through autonomous operation. The deep learning model independently processes GPR data without requiring continuous human intervention, automatically making detection decisions and generating results. This allows the system to serve itself in terms of data interpretation, freeing operators from time-consuming manual analysis tasks

The patent changes the fundamental parameters of GPR data processing by transitioning from image-based detection to direct signal-based deep learning analysis. Instead of converting GPR data to images and then detecting targets, the system processes raw GPR signals directly through CNN layers, adapting the measurement approach to work effectively with traffic-speed data acquisition while maintaining high precision

The patent moves the detection process to a different dimensional space by using deep learning feature extraction. Rather than relying on visual image characteristics, the system transforms GPR signals into a high-dimensional feature space where the CNN model can automatically identify patterns indicative of moisture damage, enabling accurate detection without intensive manual measurement