Defects identification and location of underground space for ground penetrating radar based on deep learning

Collapses of urban roads caused by defects in shallow underground spaces are extremely dangerous, and this imposes high requirements for the detection of potential defect areas. Although the characteristics of defects, including voids, water-bearing voids, hyperbola defects and loose defects, can be effectively detected using ground penetrating radar (GPR), amounts of GPR data are currently limited. In this work, a deep convolutional generative adversarial network (DCGAN) with an improved loss function is applied to augment existing GPR data to give a total of 3,256 images. More importantly, a recognition model for underground defects is proposed based on GPR B-scans and strengthened with attention modules using YOLO v5. The mean average precision (mAP) of the model is 85.4 %, a value 3.26 % higher than that of YOLO v5. The values of the average precision (AP) for voids, water-bearing voids, hyperbolae, and loose defects are 87.5 %, 86.6 %, 96.2 %, and 71.1 %, respectively. Finally, the locations and span sizes of the defects are obtained by estimating the velocity of the electromagnetic wave, and this approach is verified through a field test. The absolute error in the burial depth is less than 0.16 m, and the average error ratio is less than 15 %. The absolute error in the horizontal span is found to be lower than 0.28 m, with an average error ratio of smaller than 22 %. Consequently, the proposed method provides reasonable support for more accurate and effective detection of underground defects.

Automated summary

An improved DCGAN and attention modules are utilized to propose a recognition model for underground defects, which has been verified to provide more accurate and effective detection of such defects.