Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics

Long Xu¹  Jiabin Luo¹  Feifei Hou¹  Ruiqing Wang¹  Xinyue Jiang¹  Wentai Lei¹ 

[1] School of Computer Science and Engineering, Central South University, Changsha 410075, China;

关键词: ground penetrating radar (GPR);    hyperbola region detection;    Convolutional Neural Network (CNN);    Long Short-Term Memory (LSTM);    hyperbola classification;    diameter identification;   

DOI  :  10.3390/electronics9111804

来源: DOAJ

【 摘 要 】

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

【 授权许可】

Unknown