Classification Network Integrated with Multidimensional Attention Strategy of Underground Disaster Images.

Ground penetrating radar (GPR) is a non-destructive exploration technology that utilizes high-frequency ultra-wideband signals to detect the distribution of subsurface objects and media. Benefiting from the advantages of non-destructiveness, high efficiency, and high resolution, GPR has been widely applied in underground defect detection task of urban roads. Illustrating the radar echo wave of subsurface structure, GPR B-scan images are a main means to detect underground disaster; however, compared to natural images, automatic interpretation of GPR B-scan ones is a more challenging task because of same objects with different spectra, different objects with same spectrum as well as heavy noise pollution. Aiming to improve the accuracy of subsurface disasters detection methods, a disasters classification network, i.e., MA-ResNeXt, based on ResNetXt50 is proposed by combining multi-dimensional attention mechanism, atrous space pyramid pool and multi-scale feature extraction structure. The proposed network is trained and tested on real GPR B-scan images of three common subsurface disasters, e.g., void, cavity underneath pavement (CUP) and loosely infilled void (LIV). The comparison results show that classification accuracy of the proposed network approaches 98.2%, and illustrate that the network can effectively realize accurate classification of underground disasters. [ABSTRACT FROM AUTHOR]