  Recent advancements in deep learning have significantly improved visual
quality inspection and predictive maintenance within industrial settings.
However, deploying these technologies on low-resource edge devices poses
substantial challenges due to their high computational demands and the inherent
complexity of Explainable AI (XAI) methods. This paper addresses these
challenges by introducing a novel XAI-integrated Visual Quality Inspection
framework that optimizes the deployment of semantic segmentation models on
low-resource edge devices. Our framework incorporates XAI and the Large Vision
Language Model to deliver human-centered interpretability through visual and
textual explanations to end-users. This is crucial for end-user trust and model
interpretability. We outline a comprehensive methodology consisting of six
fundamental modules: base model fine-tuning, XAI-based explanation generation,
evaluation of XAI approaches, XAI-guided data augmentation, development of an
edge-compatible model, and the generation of understandable visual and textual
explanations. Through XAI-guided data augmentation, the enhanced model
incorporating domain expert knowledge with visual and textual explanations is
successfully deployed on mobile devices to support end-users in real-world
scenarios. Experimental results showcase the effectiveness of the proposed
framework, with the mobile model achieving competitive accuracy while
significantly reducing model size. This approach paves the way for the broader
adoption of reliable and interpretable AI tools in critical industrial
applications, where decisions must be both rapid and justifiable.
