Shower-Aware Dual-Stream Voxel Networks for Structural Defect Detection in Cosmic-Ray Muon Tomography

Abstract

We present SA-DSVN, a 3D convolutional architecture for voxel-level segmentation of structural defects in reinforced concrete using cosmic-ray muon tomography. Unlike conventional reconstruction methods (POCA, MLSD) that rely solely on muon scattering angles, our approach jointly processes scattering kinematics (9 channels) and secondary electromagnetic shower multiplicities (40 channels) through independent encoder streams fused via cross-attention. Training data were generated using Vega, a cloud-native Geant4 simulation framework, producing 4.5 million muon events across 900 volumes containing four defect types - honeycombing, shear fracture, corrosion voids, and delamination - embedded within a dense 7x7 rebar cage. A five-variant ablation study demonstrates that the shower multiplicity stream alone accounts for the majority of discriminative power, raising defect-mean Dice from 0.535 (scattering only) to 0.685 (shower only). On 60 independently simulated validation volumes, the model achieves 96.3% voxel accuracy, per-defect Dice scores of 0.59-0.81, and 100% volume-level detection sensitivity at 10 ms inference per volume. These results establish secondary shower multiplicity as a previously unexploited but highly effective feature for learned muon tomographic reconstruction.

Figures (10)

• Figure 1: Side-view schematic of the Geant4 detector geometry. A 1 m$^3$ concrete target containing a $7\times7$ rebar cage sits between two detector stations (3 planes each, spaced 100 mm apart). A 4 GeV $\mu^-$ enters from above; scattering angle and secondary shower particles are recorded on all six planes.
• Figure 2: Ground-truth voxel label maps for the five target configurations on the $20^3$ grid. Grey voxels represent the $7\times7$ rebar cage (present in all configurations). Coloured voxels indicate defect regions: orange = honeycombing (scattered bar removal), red = shear fracture (diagonal section removal), purple = corrosion void ($3\times3$ corner removal), yellow = delamination (thin air layer). Concrete voxels are rendered transparent.
• Figure 3: SA-DSVN architecture. The scattering stream (9 channels) and shower stream (40 channels) pass through independent 3-stage encoders. At the $5^3$ bottleneck, cross-attention fuses both representations. The decoder upsamples through attention-gated skip connections, with deep supervision at intermediate stages.
• Figure 4: Per-defect Dice scores across the five ablation variants. The scattering-only model falls substantially behind all configurations that include shower data.
• Figure 5: Per-defect Dice improvement when adding each stream to the baseline. The shower stream provides the bulk of discriminative power; scattering adds a smaller but consistent contribution, especially for honeycombing.