A Multi-Modal AI Inspection System for Laser Labels

This paper presents an advanced automated inspection system for laser labels, leveraging multi-modal artificial intelligence (AI) and Retrieval-Augmented Generation (RAG) to enhance the quality control process. The system integrates large language models, such as GPT -4 and Gemini, with Pinecone, a vector database, to automate the inspection process. Experimental results demonstrate that the Gemini-Pro model significantly outperforms other models, effectively utilizing RAG to enhance accuracy and select essential data. The system's performance is evaluated using a dataset of 65 images, with results showing that RAG improves the accuracy of models in detecting defects and determining status. The Gemini-Pro model exhibits the most substantial benefits from integrating RAG, with 19 out of 33 observations exhibiting enhanced outcomes. The study highlights the potential of multi-modal AI in industrial applications, setting a benchmark for future innovations in automated systems.