A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection

Researchers developed a specialized three-component pipeline for automated wind turbine blade inspection that combines object detection, spatial encoding, and a fine-tuned language model to generate structured maintenance reports. The system significantly outperforms general-purpose vision-language models, achieving 4% hallucination rate versus 65%, while running efficiently on edge hardware.

This research addresses a critical gap in industrial automation: the integration of precise visual defect detection with accurate linguistic interpretation. Traditional inspection workflows separate these tasks, requiring human experts to manually translate detection outputs into actionable maintenance reports—a process that is time-consuming, error-prone, and difficult to scale across large industrial operations. The authors' modular approach demonstrates that decoupled architectures optimized for specific subtasks can outperform monolithic end-to-end models, even when those generalist models have vastly larger parameter counts.

The architecture's design reflects broader trends in edge AI deployment, where computational efficiency and reliability matter more than raw scale. By quantizing a 1.5B-parameter model using QLoRA and training it on only 947 synthetic reports, the team achieved superior performance to a 671B-parameter API model. This finding challenges conventional wisdom that bigger models always perform better and suggests that domain-specific training data and purpose-built architectures can overcome scale disadvantages.

For industrial operators, this system reduces inspection costs and accelerates maintenance decision-making by automating report generation. The 4% hallucination rate and 8.6/10 expert score indicate reliability suitable for real-world deployment. The edge-deployable nature means operators maintain data privacy and avoid cloud dependency, addressing growing concerns about sensitive industrial data.

Future work should focus on expanding the synthetic training corpus, testing across different industrial domains beyond wind turbines, and validating long-term performance in production environments where defect distributions may shift over time.

• →Decoupled, domain-specific architectures outperform large generalist models on structured industrial inspection tasks.
• →A 1.5B-parameter quantized model achieves superior performance to a 671B-parameter baseline when properly fine-tuned on domain data.
• →The system reduces hallucination rates from 65% to 4%, making automated report generation viable for safety-critical industrial applications.
• →Edge deployment on T4 GPUs enables on-premise processing while maintaining high throughput of 47 tokens per second.
• →Retrieval-augmented fine-tuning grounds recommendations in indexed maintenance procedures, improving reliability and traceability.