GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection
Accurate detection of underground grounding lines remains a significant technical challenge due to their deep burial and small cross-sectional dimensions, which cause signal scattering in heterogeneous soil media. This results in blurred features in GPR B-scan images, impeding reliable target identi...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2223 |
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| author | Chongqin Wang Yi Guan Minghe Chi Feng Shen Zhilong Yu Qingguo Chen Chao Chen |
| author_facet | Chongqin Wang Yi Guan Minghe Chi Feng Shen Zhilong Yu Qingguo Chen Chao Chen |
| author_sort | Chongqin Wang |
| collection | DOAJ |
| description | Accurate detection of underground grounding lines remains a significant technical challenge due to their deep burial and small cross-sectional dimensions, which cause signal scattering in heterogeneous soil media. This results in blurred features in GPR B-scan images, impeding reliable target identification. To address this limitation, we propose GPR-TSBiNet, an architecture incorporating two key model innovations. We introduce GPR-Transformer (GPR-Trans), a multi-branch backbone network specifically designed for GPR B-scan processing. In the neck stage, we develop the Spatial-Depth Converted Bidirectional Feature Pyramid Network (SC-BiFPN), which integrates SPD-ADown to mitigate feature loss caused by traditional pooling-based downsampling. We employ Shape-IoU as the loss function to enhance boundary detail preservation for small targets. Comparative experiments demonstrate that GPR-TSBiNet outperforms state-of-the-art (SOTA) models YOLOv11 and YOLOv10 in detection accuracy, achieving an AP0.5 improvement of 11.6% over YOLOv11X and 27.4% over YOLOv10X. Notably, the model improves small-target APsmall to 49.4 ± 0.7%, representing a 13.4% increase over the SOTA YOLOv11 model. Finally, real-world GPR validation experiments are conducted, confirming that GPR-TSBiNet provides a reliable solution for underground grounding line detection in GPR-based target recognition. |
| format | Article |
| id | doaj-art-0c4eab5bc8dc488eb8424e5fa47424d5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-0c4eab5bc8dc488eb8424e5fa47424d52025-08-20T03:08:59ZengMDPI AGSensors1424-82202025-04-01257222310.3390/s25072223GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target DetectionChongqin Wang0Yi Guan1Minghe Chi2Feng Shen3Zhilong Yu4Qingguo Chen5Chao Chen6School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaCollege of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaAccurate detection of underground grounding lines remains a significant technical challenge due to their deep burial and small cross-sectional dimensions, which cause signal scattering in heterogeneous soil media. This results in blurred features in GPR B-scan images, impeding reliable target identification. To address this limitation, we propose GPR-TSBiNet, an architecture incorporating two key model innovations. We introduce GPR-Transformer (GPR-Trans), a multi-branch backbone network specifically designed for GPR B-scan processing. In the neck stage, we develop the Spatial-Depth Converted Bidirectional Feature Pyramid Network (SC-BiFPN), which integrates SPD-ADown to mitigate feature loss caused by traditional pooling-based downsampling. We employ Shape-IoU as the loss function to enhance boundary detail preservation for small targets. Comparative experiments demonstrate that GPR-TSBiNet outperforms state-of-the-art (SOTA) models YOLOv11 and YOLOv10 in detection accuracy, achieving an AP0.5 improvement of 11.6% over YOLOv11X and 27.4% over YOLOv10X. Notably, the model improves small-target APsmall to 49.4 ± 0.7%, representing a 13.4% increase over the SOTA YOLOv11 model. Finally, real-world GPR validation experiments are conducted, confirming that GPR-TSBiNet provides a reliable solution for underground grounding line detection in GPR-based target recognition.https://www.mdpi.com/1424-8220/25/7/2223feature extractiontransformersGPRdeep learningoptical imaging |
| spellingShingle | Chongqin Wang Yi Guan Minghe Chi Feng Shen Zhilong Yu Qingguo Chen Chao Chen GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection Sensors feature extraction transformers GPR deep learning optical imaging |
| title | GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection |
| title_full | GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection |
| title_fullStr | GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection |
| title_full_unstemmed | GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection |
| title_short | GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection |
| title_sort | gpr tsbinet an information gradient enrichment model for gpr b scan small target detection |
| topic | feature extraction transformers GPR deep learning optical imaging |
| url | https://www.mdpi.com/1424-8220/25/7/2223 |
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