AMD Multimodal Defect Agent

AMD Multimodal Defect Agent: Intelligent Industry Copilot In high-speed modern manufacturing environments, identifying a defective assembly part is only half the operational challenge. When an automated inspection camera flags an anomaly, factory operators often face critical delays trying to determine why the failure occurred. Manual troubleshooting leads to prolonged operational downtime, increased scrap rates, and high maintenance overhead. The AMD Multimodal Defect Agent bridges the critical gap between visual computer vision inspection and operational intelligence. Our solution integrates real-time visual anomaly detection with dynamic Retrieval-Augmented Generation (RAG) across historical maintenance logs to deliver an end-to-end industrial copilot. Key System Capabilities: Real-Time Visual Anomaly Analysis: Processes live part feeds against baseline reference images using OpenCV differential analysis to compute dynamic defect severity scores. Contextual Root-Cause Retrieval: Automatically queries local machine maintenance records (maintenance_logs.txt) whenever anomaly scores exceed safety thresholds. Actionable Copilot Guidance: Correlates visual defect patterns with historical mechanical logs (e.g., guide rail misalignment, belt vibration) to provide line operators with immediate, actionable repair recommendations. Hardware Architecture Strategy for AMD: Architected for edge-to-cloud deployment across the AMD hardware ecosystem, the pipeline runs natively on x86 architectures. It is specifically designed to be deployed on AMD Kria™ K26 SOMs and AMD Ryzen™ AI PCs at the factory edge for sub-millisecond, low-latency visual defect inference without cloud dependency. Furthermore, the PyTorch-compatible computer vision engine scales seamlessly onto AMD Radeon™ and AMD Instinct™ GPUs via AMD ROCm™ for high-throughput multi-camera industrial environments.