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The system monitors multiple docks in logistics warehouses, identifying pallet loads in real-time and providing feedback for efficient truck loading to prevent errors. It enhances traditional Auto-ID by incorporating pallet class classification, detection, and size estimation, utilizing foundational models, such as DINOv2, MobileNetV3, SAM, Depth Anything V2, and Depth Pro, alongside few-shot learning to generate efficient training datasets with minimal labeling effort. We propose three key innovations: (1) an embedding analysis approach for precise load classification; (2) DINOv2-based visual feature detection solutions for bounding box estimation, and (3) a depth-guided segmentation strategy for improved load isolation and measurement accuracy. Experimental results on a curated industrial dataset compiled by us demonstrate high mean average precision, with optimized trade-offs between latency and accuracy for fog computing constraints. 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