Pattern Recognition Methods for GPR-Based Detection of Subsurface Drainage Pipes

This paper presents the development and application of pattern recognition techniques to identify hyperbolic shapes in Ground Penetrating Radar (GPR) data, which are characteristic signatures of underpavement drainage pipes. The proposed method involves several stages of image processing. It begins with the removal of background noise and enhancement of signal contrast through vertical data selection and background noise subtraction. The signal contrast is further enhanced by squaring the data values, culminating in the conversion of the enhanced images into a binary format to facilitate the detection of hyperbolic patterns. By leveraging improved background reduction methods, the study pre-selects potential target areas, thereby limiting the analysis to regions of interest and enhancing computational efficiency. Field tests conducted on highway surveys demonstrate the effectiveness of this method, successfully identifying known drainage pipes and detecting additional potential targets for further investigation. The results indicate that while the presence of a hyperbolic return strongly suggests a detection, the absence of such a return does not conclusively indicate a false alarm. This underscores the importance of thoroughly investigating all potential target zones identified. The study concludes that integrating pattern recognition into an automated data processing system can significantly improve the accuracy and efficiency of GPR-based detection. Such integration would aid in rapidly identifying clear targets and focusing field investigations on uncertain zones, thereby enhancing the overall effectiveness of subsurface drainage pipe detection.