Abstract
Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the geological exploration applications, it needs to locate and identify underground objects according to electromagnetic wave characteristics from the ground-penetrating radar (GPR) images. Currently, a few robust AI approach can detect targets by real-time with high precision or automation for GPR images recognition. This paper proposes an approach that can be used to identify parabolic targets with different sizes and underground soil or concrete structure voids based on you only look once (YOLO) v3. With the TensorFlow 1.13.0 developed by Google, we construct YOLO v3 neural network to realize real-time pattern recognition of GPR images. We propose the specific coding method for the GPR image samples in Yolo V3 to improve the prediction accuracy of bounding boxes. At the same time, K-means algorithm is also applied to select anchor boxes to improve the accuracy of positioning hyperbolic vertex. For some instances electromagnetic-vacillated signals may occur, which refers to multiple parabolic electromagnetic waves formed by strong conductive objects among soils or overlapping waveforms. This paper deals with the vacillating signal similarity intersection over union (IoU) (V-IoU) methods. Experimental result shows that the V-IoU combined with non-maximum suppression (NMS) can accurately frame targets in GPR image and reduce the misidentified boxes as well. Compared with the single shot multi-box detector (SSD), YOLO v2, and Faster-RCNN, the V-IoU YOLO v3 shows its superior performance even when implemented by CPU. It can meet the real-time output requirements by an average 12 fps detected speed. In summary, this paper proposes a simple and high-precision real-time pattern recognition method for GPR imagery, and promoted the application of artificial intelligence or deep learning in the field of the geophysical science.
Highlights
In the application of ground-penetrating radar (GPR) engineering detection, the following three cases are the most common: (1) Inspection of the atypical situation of reinforced concrete structures such as bridges, tunnels, or public roads, or the number of steel bars inside those structures;(2) locating certain objects underground, such as archaeological research; (3) evaluating and measuring the distribution of hollows, voids, or soil firmness in highways, bridges, and tunnels
Pham and Lefèvre (2018) used the faster-RCNN framework to detect hyperbola reflections from many B-Scans generated from gprMax toolbox and the results show that faster-RCNN
you only look once (YOLO) v3 is a classical pattern-recognition algorithm based on darknet-53 convolutional neural network (CNN) architecture proposed by Joseph Redmon in 2018 [15]
Summary
In the application of ground-penetrating radar (GPR) engineering detection, the following three cases are the most common: (1) Inspection of the atypical situation of reinforced concrete structures such as bridges, tunnels, or public roads, or the number of steel bars inside those structures;. (2) locating certain objects underground, such as archaeological research; (3) evaluating and measuring the distribution of hollows, voids, or soil firmness in highways, bridges, and tunnels. The outcomes, after GPR detection, are often judged by the worker’s experience to recognize the location and size information of the target [1,2]. These kinds of evaluations using GPR image are not Sensors 2020, 20, 6476; doi:10.3390/s20226476 www.mdpi.com/journal/sensors.
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