Buried object detection from GPR images using improved U-net

Shihang Li; Qinghua Liu; Renjie Li

doi:10.1117/12.2667354

10 February 2023 Buried object detection from GPR images using improved U-net

Shihang Li, Qinghua Liu, Renjie Li

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522B (2023) https://doi.org/10.1117/12.2667354
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Buried object detection methods based on deep learning require a lot of annotated data, and most of them rely on pretrained models. To solve these problems, a buried object detection method that only needs a small amount of annotated data and has a short training time is proposed. This method integrates the attention mechanism into the U-net model, obtains the pixel-to-pixel predicted grayscale, and finally extracts the region of interest for target localization. The experimental results show that this method can accurately detect buried objects with only a small amount of annotated data in the actual B-scan images of ground penetrating radar.

Citation Download Citation

Shihang Li, Qinghua Liu, and Renjie Li "Buried object detection from GPR images using improved U-net", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522B (10 February 2023); https://doi.org/10.1117/12.2667354

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