Real time approach for underground objects detection from vehicle-borne ground penetrating radar

doi:10.11947/j.AGCS.2020.20190293

Abstract

Abstract: Urbanization has triggered great development and changes in underground space. Exploring the types and positions of underground targets is of vital importance to urban underground security and utilization. GPRs (ground penetrating radar) are widely used in exploring underground space because of its advantages of rapid data collection, convenience, high imaging resolution and non-destructive inspection. However, the heavy manual interpretation costs of object detection from GPR limit the GPR applications in large-scale urban underground objects detection. This paper analyzes and determines seven typical types of urban road underground target that can be detected in GPR images (e.g. rainwater wells, cables, etc.). According to the characteristics of its reflected signals, the underground target in the GPR data of the 400 MHz band acquired by GSSI SIR30 in a typical urban road environment are labeled to construct the training dataset with seven categories and 3033 training samples. With the transfer learning method, the pre-trained Darknet 53 network parameters are fine-tuned, and the end-to-end YOLOV3 detection method is used to automatically extract and locate the underground targets. Finally, the experimental verification was carried out using the GPR data of the 400 MHz band collected by GSSI SIR30 in Caitian Road, Futian District, Shenzhen. Experiments show that the proposed deep learning detection method detects the buried objects from GRP data effectively, in terms of 85% of recall and precision,and the detection speed of 16FPS.

Key words: ground penetrating radar, underground object detection, convolutional neural network, deep learning, urban space security

CLC Number:

P227

YANG Bisheng, ZONG Zeliang, CHEN Chi, SUN Wenlu, MI Xiaoxin, WU Weitong, HUANG Ronggang. Real time approach for underground objects detection from vehicle-borne ground penetrating radar[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 874-882.

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