GPR Image Recovery Effect on Faster R-CNN- Based Buried Target Detection


Creative Commons License

Kumlu D.

JOURNAL OF ELECTROMAGNETIC ENGINEERING AND SCIENCE, vol.22, no.5, pp.591-598, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.26866/jees.2022.5.r.127
  • Journal Name: JOURNAL OF ELECTROMAGNETIC ENGINEERING AND SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Page Numbers: pp.591-598
  • Keywords: Buried Target Detection, Deep Learning, Faster R-CNN, Low Rank Data Recovery, Matrix Completion, MATRIX COMPLETION, DATA RECONSTRUCTION
  • Istanbul Technical University Affiliated: Yes

Abstract

Measurements acquired through ground-penetrating radar (GPR) may contain missing information that needs to be recovered before the implementation of any post-processing method, such as target detection, since buried target detection methods fail and cannot produce desired results if the input GPR image contains missing information. This study proves that the recovery of missing information in a GPR image has a direct influence on the performance of subsequent target detection methods. Thus, state-of-the-art matrix completion methods are applied to the GPR image with missing information in both pixel-and column-wise cases with different missing rates, such as 30% and 50%. After the GPR image is successfully recovered, the faster region-based convolutional neural network (Faster R-CNN) target detection method is applied. The performance correlation between matrix completion accuracy and the target detection method's confidence score is analyzed using both quantitative and visual results. The obtained results demonstrate the importance of GPR image recovery prior to any post-processing implementation, such as target detection.