A Study of Automatic Recognition and Localization of Pipeline for Ground Penetrating Radar based on Deep Learning

• Published in: IEEE geoscience and remote sensing letters Vol. 19; p. 1
• Main Authors: Hu, Haobang, Fang, Hongyuan, Wang, Niannian, Liu, Hai, Lei, Jianwei, Ma, Duo, Dong, Jiaxiu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna measurements; Character recognition; Convolutional neural networks; Deep learning; Faster R-CNN; Feature extraction; Ground penetrating radar; Horizontal orientation; Localization; Location awareness; Machine learning; Methods; Optical character recognition; pipeline; Pipelines; Radar; Real-time systems; recognition and localization
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3198439

This letter proposes a method based on deep learning for the automatic recognition and localization of underground pipelines using the ground penetrating radar (GPR). Firstly, an automatic recognition model with an average precision (AP) of 0.9256 is proposed and trained based on Faster R-CNN. The feature extraction is optimized by the Attention-guided Context Feature Pyramid Network (ACFPN), and the cascade structure is used to improve the detection frame regression accuracy. Moreover, using Tesseract OCR, a positioning model is developed based on recognition results to obtain the burial and horizontal position of the pipeline. Furthermore, on-site experiments were carried out on real embedded pipes to verify the feasibility and effectiveness of the developed method. The absolute error of the localization data is lower than 11 cm, and the average error ratio is smaller than 12%. Consequently, it is demonstrated that the proposed method is considerably automatic, efficient, and reliable for the recognition and localization of underground pipelines.