With the rapid development of industrial automation, few-shot object detection has emerged as a promising solution for recognizing novel categories using only limited annotated data. However, existing approaches often suffer from high computational complexity and limited adaptability when deployed in resource-constrained industrial environments. To achieve precise detection, efficiency, and security, this article proposes a collaborative computing framework based on an edge-cloud dual-prototype graph convolutional network (EC-DP-GCN) for few-shot object detection with hierarchical knowledge embedding. The framework comprises three key components: a device-EC architecture, a positive-negative prototype (PNP) module, and a class-prototype-sample-driven hierarchical graph (CPS-HG) module. Specifically, the PNP module explicitly models intraclass diversity by constructing discriminative positive and negative prototypes from limited support samples, thereby enhancing prototype representativeness. In addition, we further introduce the CPS-HG module, which treats the dual prototypes as class-based prior knowledge and models the relationships among samples through a hierarchical graph structure encompassing class, prototype, and sample levels. This design effectively expands the semantic margins in the embedding space to improve knowledge-guided detection. Extensive experiments on the PASCAL VOC and MS COCO benchmarks demonstrate that EC-DP-GCN significantly outperforms strong baselines and previous state-of-the-art methods, achieving an average improvement of 1.1% in 10-shot detection scenarios.
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