From Few-Shot to Zero-Shot Pallet Load Recognition: A Deployed Embedding-Based Vision System for Industrial Logistics

Automated pallet load recognition is a critical task in industrial logistics, but the deployment of conventional deep learning systems is often unfeasible. Their reliance on large, manually annotated datasets creates a prohibitive bottleneck in terms of cost and time, especially in dynamic environments where product lines frequently change. To overcome this challenge, we introduce a highly flexible, dual-mode vision system built upon dense patch embeddings. Our primary, few-shot approach leverages features from the CAPI vision model to construct a compact memory bank from as little as a single labeled example per class. Classification is then performed via a simple yet highly effective $k$-nearest neighbor search. For annotation-free scenarios, we also propose a zero-shot mode that identifies the load by finding the rectangular region that minimizes intra-class feature variance. We demonstrate state-of-the-art performance on a new, challenging industrial dataset, where our few-shot method attains a $mAP_{50-95}$ over 90\% with only one support image per class. Additionally, the fully unsupervised approach achieves a notable $mAP_{50-95}$ of up to 75\%. The system's robustness and practical value were validated through its successful deployment in high-stakes, real-world scenarios. Our findings establish a basis for lightweight solutions that support the rapid, data-efficient integration of new vision systems into industrial workflows.