# A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures

Source: [A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures](<https://www.sota2.com/research/paper/69d5e0f57d0172140ec7596d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-09T05:00:04.534Z

Authors: Wenbo Zhang, Zekun Long, Zican Liu, Yangchen Zeng, Keyi Hu

Published: 2026-04-07T06:33:50.000Z

## Abstract

Subsurface defect detection via Ground Penetrating Radar is challenged by "weak signals" faint diffraction hyperbolas with low signal-to-clutter ratios, high wavefield similarity, and geometric degradation. Existing lightweight detectors prioritize efficiency over sensitivity, failing to preserve low-frequency structures or decouple heterogeneous clutter. We propose WSA-Net, a framework designed to enhance faint signatures through physical-feature reconstruction. Moving beyond simple parameter reduction, WSA-Net integrates four mechanisms: Signal preservation using partial convolutions; Clutter suppression via heterogeneous grouping attention; Geometric reconstruction to sharpen hyperbolic arcs; Context anchoring to resolve semantic ambiguities. Evaluations on the RTSTdataset show WSA-Net achieves 0.6958 mAP@0.5 and 164 FPS with only 2.412 M parameters. Results prove that signal-centric awareness in lightweight architectures effectively reduces false negatives in infrastructure inspection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on Roboflow-GPR KSLMA](<https://www.sota2.com/research/sota/object-detection-on-roboflow-gpr-kslma.md>): Object Detection; Roboflow-GPR KSLMA; Precision: 61.25.

- [Object Detection on RTSTdataset (test)](<https://www.sota2.com/research/sota/object-detection-on-rtstdataset-test.md>): Object Detection; RTSTdataset (test); Precision: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.05490>)

[PDF](<https://arxiv.org/pdf/2604.05490.pdf>)