EvoInspect: A Unified Self-Evolving Multi-Agent Framework for Industrial Hardware Inspection

Human experts can accumulate experience from open-ended and complex inspection tasks, thereby continually improving their inspection capabilities. Actually, industrial hardware inspection faces challenges including open-set defect categories, complex defects (tiny, high-gloss, low-light, or blurred), high inference cost, and difficulty in accumulating experience post-deployment. Existing end-to-end inspection methods typically couple image restoration with defect recognition in a single forward pass, limiting generalization to unseen components or composite defects; cascade “restore-then-detect” approaches often rely on static preprocessing, hindering adaptive adjustment based on scenario, defect morphology, and historical failures. To address this, we propose EvoInspect, the first memory-enhanced multi-modal multi-agent framework for industrial defect object detection. It leverages a multi-modal agentic memory mechanism to distill self-evolving scenario-specific Defect Detail Restoration decisions and dynamically grounded trajectories, enabling “accumulating experience like human experts.” Specifically, (1) we introduce a dynamic Defect Detail Restoration strategy, including adaptive slicing for tiny defects, highlight suppression, low-light enhancement, super-resolution, and binarization, combined with a coarse-to-fine token-efficient grounding mechanism for efficient defect capture; (2) we design a dual-channel distillation mechanism unifying restoration and detection (perception and execution) experience, incorporating both successful and failed validation trajectories to support system self-evolution. Experiments on bearing surfaces, PCBs, solar electroluminescence (solar EL), and magnetic tile datasets show that EvoInspect consistently outperforms strong baselines in defect localization recall, inference accuracy, while demonstrating cross-scenario adaptability and offline continual improvement.