Sensors, Vol. 20, Pages 6476: Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Sensors, Vol. 20, Pages 6476: Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow Sensors doi: 10.3390/s20226476 Authors: Yuanhong Li Zuoxi Zhao Yangfan Luo Zhi Qiu 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...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research