EDGRU: edge-guided dual graph reasoning U-Net with spatial graph attention for infrared small target detection
Abstract
Detecting infrared small targets continues to be difficult because of indistinct object contours, intense background interference, and restricted multi-scale feature extraction. To tackle such problems, this work introduces EDGRU, an edge-oriented dual graph reasoning U-Net which simultaneously utilizes edge information and dual graph logic, and spatial graph attention for robust feature representation. Specifically, a multi-directional multi-order gradient edge module is designed to preserve weak structural details and suppress background interference. A dual graph reasoning framework enhances both local spatial dependencies and long-range semantic relationships, while spatial graph attention further strengthens topology-aware feature interaction to improve target-background discrimination. Empirical evaluations conducted on the NUAA-SIRST and NUDT-SIRST benchmarks reveal that EDGRU achieves the highest detection probability of 96.58% and 98.84%, respectively, while remaining competitive on other metrics, demonstrating enhanced resilience against complex environments and target scale fluctuations. The source code for this project is accessible at https://github.com/Huang-1015/EDGRU.git .
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BibTeX
@article{huang2026edgru,
title = {{EDGRU: edge-guided dual graph reasoning U-Net with spatial graph attention for infrared small target detection}},
author = {Xinying Huang and Jianhua Song and Xinrong Fu and Yizhe Lu and Lei Yuan},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76517-6},
url = {https://www.nature.com/articles/s41598-026-76517-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - EDGRU: edge-guided dual graph reasoning U-Net with spatial graph attention for infrared small target detection
AU - Xinying Huang
AU - Jianhua Song
AU - Xinrong Fu
AU - Yizhe Lu
AU - Lei Yuan
PY - 2026
DA - 2026/10/27
JO - Scientific Reports
DO - 10.1038/s41598-026-76517-6
UR - https://www.nature.com/articles/s41598-026-76517-6
AB - Detecting infrared small targets continues to be difficult because of indistinct object contours, intense background interference, and restricted multi-scale feature extraction. To tackle such problems, this work introduces EDGRU, an edge-oriented dual graph reasoning U-Net which simultaneously utilizes edge information and dual graph logic, and spatial graph attention for robust feature representation. Specifically, a multi-directional multi-order gradient edge module is designed to preserve weak structural details and suppress background interference. A dual graph reasoning framework enhances both local spatial dependencies and long-range semantic relationships, while spatial graph attention further strengthens topology-aware feature interaction to improve target-background discrimination. Empirical evaluations conducted on the NUAA-SIRST and NUDT-SIRST benchmarks reveal that EDGRU achieves the highest detection probability of 96.58% and 98.84%, respectively, while remaining competitive on other metrics, demonstrating enhanced resilience against complex environments and target scale fluctuations. The source code for this project is accessible at https://github.com/Huang-1015/EDGRU.git .
SN - 2045-2322
ER -