Advancing Welding Defect Detection in Maritime Operations via Adapt-WeldNet and Defect Detection Interpretability Analys

Source: Kamal Basha S, Athira Nambiar et al.; arXiv, August 1, 2025 arXiv

Summary

This study addresses an important industrial context: welding defect detection in maritime and offshore operations, where joint integrity in piping and structural components is critical, safety margins are tight, and inspection often challenging due to environmental conditions (humidity, salt, etc.). Traditional non-destructive testing (NDT) sometimes fails to detect subtle or internal defects; furthermore, many ML/AI-based defect detection systems are “black boxes,” lacking interpretability. The paper proposes Adapt-WeldNet, a framework that combines optimal model selection, adaptive optimizers, transfer learning, and an accompanying Defect Detection Interpretability Analysis (DDIA) module. The goal: improve detection accuracy and provide explainable outputs that experts (e.g. certified ASNT NDE Level II) can use to validate decisions. arXiv

Key contributions and findings:

• The authors systematically evaluated various pre-trained architectures, optimizers, and transfer learning strategies to find models with strong performance under maritime weld images (likely with added noise, weathering, lighting challenges). Adapt-WeldNet emerges as a model configuration showing high defect detection accuracy. arXiv

• They incorporated interpretability through tools like Grad-CAM and LIME, which help illuminate which image regions drive decisions (i.e. which areas of a weld are being flagged and why). This helps users (inspectors, engineers) to trust automated detection when they can see the reasoning. The model is validated by domain experts. arXiv

• The framework includes a Human-in-the-Loop (HITL) component: domain experts can review flagged defects, provide feedback, and improve model calibration. This contributes to safety, fairness, and accountability. arXiv

Understanding & Analysis

This study appeals to both practical welding operations and the rising importance of trustworthy AI. Some reflections:

1. Safety & reliability in harsh environments: Maritime environments are challenging: wet, salty air, variable lighting, possibly lower access for inspections. A model that can reliably detect defects (surface and possibly near-surface) under such conditions is valuable. Adapt-WeldNet aims to do just that.

2. Interpretability is central: One of the main criticisms of AI in safety-critical systems is that models make decisions without easy explanations, making users distrustful or risking false positives/negatives. Integrating tools like Grad-CAM/LIME and having domain expert validation helps build trust, which is essential in regulatory and safety contexts (ship classification societies, marine insurers, etc.).

3. Transfer learning & model selection: Given that there is often limited labeled data in specialized contexts (e.g. offshore welds), re-using pre-trained networks and fine-tuning them is efficient. But selecting which backbone model, which optimizer, etc., can make a significant difference in performance. This work’s systematic evaluation is a strength.

4. Human-in-the-Loop (HITL): Automation is powerful, but in welding defect detection (especially for safety-critical components), human oversight remains necessary. HITL ensures that the AI assists, rather than replaces, human judgment; feedback loops can improve the model over time.

5. Real-world implications:

   • Inspectors can perform quicker inspections with computerized assistance, reducing downtime (vessel, offshore platform) and costs.

   • Early detection of defects helps prevent failures, leaks, or catastrophic breakdowns, especially in offshore oil/gas or shipping.

   • Regulatory bodies might adopt similar frameworks to certify AI tools, if interpretability and expert validation are built in.

Limitations & Caveats

• Image data diversity: Maritime welds vary widely in material, geometry, lighting, corrosion, etc. The model must be robust to these variations; overfitting to a narrow set of images is a risk.

• Depth of defects: If defects are internal or subsurface and not visible in images, optical methods may miss them. Supplementary sensor modalities (ultrasound, radiography) may still be necessary.

• Compute and deployment constraints: Running models in field conditions (on ships/offshore platforms) may face power, connectivity, and hardware limitations. Ensuring efficient model size, inference speed, and rugged hardware is necessary.

• False positives/negatives cost: In inspection settings, false negatives can be dangerous; false positives can be costly (unnecessary repair). Balance must be carefully managed.

Conclusion

This case study advances welding defect detection by combining performance with interpretability, a crucial step for safe adoption in maritime and offshore contexts. Adapt-WeldNet, with its model selection, interpretability, and human oversight components, offers a promising path forward. For industries with high safety demands, such frameworks are likely to become standard. It also suggests that future welding QA systems will not only need good detection accuracy, but transparency, domain expert integration, and robustness under adverse conditions.