Paper
30 August 2023 The underground object detection method with a self-attention mechanism
Renjie Li, Qinghua Liu, Shihang Li
Author Affiliations +
Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127970P (2023) https://doi.org/10.1117/12.3007478
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China
Abstract
To solve the problem that the few measured B-Scan annotated data obtained from the ground-penetrating radar are difficult to train in a large-scale model, we propose an underground target classification and detection method that requires only a small number of data sets and a short training time. This method integrates the self-attention mechanism and network layer skip connection into the SegNet model called GPRSNet, completes pixel-to-pixel classification prediction, and finally performs target localization based on the classification results. The results show that the method can still classify and label underground targets with only a small number of labeled data sets. Due to the use of real measurement data for model training, the method can be effectively used in practical applications to detect targets in ground penetrating radar images.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Renjie Li, Qinghua Liu, and Shihang Li "The underground object detection method with a self-attention mechanism", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127970P (30 August 2023); https://doi.org/10.1117/12.3007478
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KEYWORDS
Target detection

Object detection

Education and training

Ground penetrating radar

Roads

Feature extraction

Image segmentation

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