⚡ electrical engineering

Interpretable Hybrid CNN Transformer Framework with Cross Attention Fusion and Uncertainty Quantification for Multi Mechanism Corrosion and Erosion Detection

This paper introduces CorroSense-Net, an interpretable hybrid CNN-Transformer framework featuring a novel Cross-Attention Fusion module and uncertainty quantification that achieves state-of-the-art accuracy in distinguishing between pristine, erosion, and corrosion states in industrial pipelines, thereby addressing the limitations of existing single-model approaches in safety-critical inspection.

Original authors: Hardik Gohel, Bryant Pineda, Leonel Lagos, Dwayne McDaniel, Aparna Aravelli

Published 2026-08-06

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CC BY 4.0

Original authors: Hardik Gohel, Bryant Pineda, Leonel Lagos, Dwayne McDaniel, Aparna Aravelli

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the hidden skeleton of our modern world: massive pipes carrying oil, gas, and even radioactive waste, buried underground or stretching across oceans. Over time, these metal giants get sick. They suffer from two main ailments: corrosion, which is like a slow, chemical rust eating away at the metal, and erosion, which is like sandpaper blasting the surface off due to fast-moving fluids. If we don't catch these problems early, the pipes can burst, causing disasters that cost the global economy trillions of dollars.

To find these hidden sicknesses, we usually need human inspectors to take photos and squint at them, looking for tiny pits or rust spots. But humans get tired, and their eyes can play tricks on them. Recently, scientists have tried teaching computers to do this job using "Deep Learning." Think of these computer brains as two different types of detectives. One type, called a CNN (Convolutional Neural Network), is great at spotting tiny details like the texture of rust or a scratch, but it sometimes misses the big picture. The other type, called a Transformer (or ViT), is amazing at understanding the whole scene and how different parts connect, but it can sometimes miss the tiny, gritty details. For a long time, researchers tried to use just one detective or simply glued their notes together, but that wasn't quite enough to solve the mystery perfectly.

This is where a new team of researchers steps in with a clever solution called CorroSense-Net. Instead of forcing the two detectives to work separately or just reading each other's notes, they built a system where the two detectives talk to each other in real-time. They use a special "Cross-Attention" module, which acts like a super-powered translator. When the texture detective sees a weird spot, it can ask the context detective, "Does this look like a scratch or just a shadow?" and the context detective replies, "Nope, that's definitely a scratch because of the surrounding pattern."

The team tested this new hybrid brain on 487 photos taken with a regular smartphone in a lab that simulated these pipe problems. They didn't just ask the computer to say "sick" or "healthy." Instead, they taught it to distinguish between three specific states: a pristine (perfectly healthy) pipe, a pipe hit by erosion (sandpaper-like damage), and a pipe suffering from chemical corrosion (rust). The results were impressive. The new system correctly identified the condition of the pipes about 96% of the time, significantly better than using either detective alone or previous hybrid attempts.

But the team didn't stop at just being accurate; they wanted the computer to be honest about how sure it was. They added a feature that lets the computer say, "I'm 99% sure this is rust," or "I'm only 50% sure, so a human should double-check this." This is crucial because in safety-critical jobs, a wrong guess can be dangerous. They also made the computer "explain its work" by highlighting exactly which parts of the photo made it think a pipe was sick. When they compared these computer highlights to those made by human experts, the new system matched the experts much better than older methods did.

In short, this paper shows that by making two different types of AI detectives work together as a team—rather than just sitting side-by-side—we can build a smarter, more reliable tool for keeping our industrial infrastructure safe. It's a step toward a future where a simple smartphone photo can tell us exactly what's wrong with a pipe, how bad it is, and how much we should trust the diagnosis.

Technical Summary: CorroSense-Net

Problem Statement

Corrosion and flow-induced erosion of metallic infrastructure impose an estimated US$2.5 trillion annual global burden and are primary causes of catastrophic failures in sectors such as nuclear waste transport, oil and gas, and marine infrastructure. While automated visual inspection has advanced through Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), existing approaches suffer from distinct limitations:

CNNs excel at capturing local texture (e.g., rust granularity) but struggle with global context and long-range dependencies.
ViTs model long-range spatial dependencies effectively but are often texture-insensitive and data-hungry, performing poorly on small, specialized industrial datasets without massive pretraining.
Existing Hybrid Models (e.g., HCT-Net) typically rely on static feature concatenation, failing to exploit adaptive cross-modal interaction between local and global features.
Classification Limitations: Prior work often treats degradation as a binary "clean vs. corroded" problem, conflating physically distinct mechanisms like particle-impingement erosion and chemical corrosion, which have different diagnostic signatures.
Trust and Interpretability: Most frameworks lack uncertainty quantification (UQ) and quantitative interpretability, relying on qualitative visualizations (Grad-CAM) that are rarely benchmarked against expert annotations.

Methodology: CorroSense-Net

The authors propose CorroSense-Net, a hybrid CNN–Transformer framework designed for multi-mechanism corrosion and erosion detection under realistic, small-data industrial conditions.

1. Architecture

The framework employs a parallel two-branch network:

CNN Branch: A ResNet-50 backbone (ImageNet-pretrained) extracting local spatial features.
Transformer Branch: A ViT-B/16 backbone (ImageNet-21k pretraining, ImageNet-1k fine-tuned) extracting global contextual tokens.
Cross-Attention Fusion (CAF) Module: A novel module replacing static concatenation. It performs bidirectional gated attention:
- Stage 1: CNN features attend to ViT tokens.
- Stage 2: ViT tokens attend to CNN features.
- Stage 3: Learnable gating coefficients ( $\alpha, \beta$ ) dynamically weight the fused descriptors based on the input image characteristics (e.g., prioritizing CNN for texture-heavy erosion, ViT for uniform pristine surfaces).
Classification Head: A fused descriptor passes through a GELU layer and dropout to output a three-class probability distribution: Pristine, Particle-Impingement Erosion, and Chemical Corrosion.

2. Uncertainty and Interpretability

Uncertainty Quantification: The framework integrates Monte Carlo (MC) Dropout at inference (30 stochastic forward passes) to generate predictive entropy and confidence intervals, enabling entropy-based triage.
Quantitative Interpretability: The system generates Grad-CAM heatmaps for the CNN branch, ViT branch, and fused output. Unlike prior qualitative studies, this work quantitatively scores the alignment of these heatmaps against expert-annotated degradation masks using Intersection-over-Union (IoU).

3. Dataset and Experimental Setup

Data Source: A domain-specific corpus of 487 smartphone-captured images (Apple iPhone 13 Pro) from two purpose-built laboratory rigs:
- Erosion Flow Loop System (EFLS): Simulates particle-laden slurry flow (nuclear waste surrogate).
- Chemical Corrosion Loop System (CCLS): Simulates accelerated chloride attack.
Label Verification: Labels were assigned based on rig provenance and cross-verified by a blinded second inspector, with independent physical verification via ultrasonic thickness measurements.
Evaluation Protocols:
- Stratified five-fold cross-validation.
- Leave-one-coupon-out cross-validation (spatial generalization).
- Zero-shot inference on two public datasets (Bianchi–Hebdon and NEU Surface Defect Database).
- Robustness testing against Gaussian blur, illumination shifts, and JPEG compression.

Key Results

Under stratified five-fold cross-validation, CorroSense-Net achieved:

Macro-F1 Score: $0.961 \pm 0.014$
Accuracy: $0.964 \pm 0.012$
ROC-AUC: $0.991 \pm 0.006$
Expected Calibration Error (ECE): $0.029$ (indicating highly reliable confidence estimates).

Comparative Performance:
CorroSense-Net statistically significantly outperformed ResNet-50, ViT-B/16, EfficientNet-B3, Swin-Tiny, and the concatenation-based HCT-Net (Macro-F1 $0.931$) after Bonferroni correction.

Key Findings:

Mechanism Differentiation: The model achieved a 0.928 recall for the "Pristine" class and, critically, misclassified zero corrosion images as pristine, eliminating the most consequential error mode in safety-critical inspection.
Fusion Efficacy: The CAF module improved Grad-CAM alignment with expert masks by 7.5 IoU percentage points over concatenation-based fusion.
Robustness: Under severe Gaussian blur ( $\sigma=3.0$ ), CorroSense-Net retained a Macro-F1 of 0.872, significantly outperforming ResNet-50 (0.681) and ViT-B/16 (0.643). The gating mechanism adaptively shifted weight toward the ViT branch as local texture degraded.
Generalization: Zero-shot inference on external public datasets confirmed cross-domain generalization, with CorroSense-Net maintaining the highest F1 scores compared to baselines.

Significance and Claims

The paper claims CorroSense-Net establishes a template for interpretable, uncertainty-aware AI in safety-critical industrial inspection. Its significance is framed around four coordinated innovations:

Physically Grounded Formulation: Moving beyond binary classification to distinguish between pristine, erosion, and chemical corrosion states.
Adaptive Fusion: Demonstrating that bidirectional gated cross-attention is superior to static concatenation for integrating local texture and global context.
Trustworthy AI: Providing per-prediction uncertainty estimates that support automated triage workflows and align with emerging AI governance frameworks (e.g., EU AI Act, NIST AI RMF).
Quantitative Explainability: Establishing a quantitative baseline for interpretability by scoring Grad-CAM outputs against expert annotations, a metric previously absent in corrosion detection literature.

The authors position the framework as a scalable, low-cost solution deployable on commodity hardware using consumer-grade smartphone imagery, suitable for nuclear waste transport, oil and gas, and marine infrastructure monitoring. They acknowledge limitations regarding the modest dataset size and single-facility origin, outlining future work in federated learning, continuous severity regression, and edge deployment optimization.