A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation left to human experts. This paper describes a decoupled, edge-deployable pipeline for wind turbine blade inspection built from three components that each handle a distinct sub-task. The Eyes a YOLO26-x-obb oriented bounding-box detector localizes defects at dataset-native resolution. The Bridge a deterministic, parameter-free encoding module
The increasing sophistication of vision-language models and the demand for greater automation in industrial settings are converging to enable unified defect detection and reporting.
This development streamlines industrial inspection processes, reduces reliance on human linguistic interpretation, and improves efficiency, accuracy, and scalability across critical infrastructure maintenance.
Industrial inspection workflows that previously required separate defect localization and human-driven report generation can now be automated end-to-end with increased precision and reduced operational costs.
- · Industrial automation companies
- · Manufacturers of critical infrastructure
- · AI/ML developers
- · Maintenance service providers
- · Manual inspection providers
- · Traditional industrial software vendors
Automated inspection reduces human error and accelerates maintenance cycles for complex machinery like wind turbines.
The cost savings and efficiency gains could lead to broader adoption of AI-powered inspection in other high-value asset management sectors.
This could enable predictive maintenance at scale, extending asset lifespans and improving the overall reliability of industrial infrastructure, with potential implications for insurance models.
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Read at arXiv cs.LG