Decoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
It provides a practical solution for industrial object detection with minimal supervision, enabling rapid onboarding of new objects without large annotated datasets or CAD models.
The paper tackles few-shot object detection in industrial scenarios where annotated data is scarce. Their method, using vision foundation models for prototype matching, achieves a 6.9% improvement in AP over state-of-the-art training-free methods on three industrial datasets.
Industrial object detection systems typically rely on large annotated datasets, which are expensive to collect and challenging to maintain in industrial scenarios where the inventory of objects changes frequently. This work addresses the challenge of few-shot object detection in such industrial scenarios, where only a limited number of labeled samples are available for newly introduced objects. We present a detection framework that leverages vision foundation models to recognize objects with minimal supervision. The method constructs class prototypes from a small set of reference samples by extracting feature representations. For a given query scene during inference, object regions are generated using a segmentation model, and feature embeddings are extracted and matched with class prototypes using similarity matching. We evaluate the detection method on three established industrial datasets from the Benchmark for 6D Object Pose Estimation benchmark following the official 2D object detection evaluation protocol. We demonstrate competitive detection performance, improving AP by 6.9% compared to the state-of-the-art training-free detection methods. Furthermore, the presented method is able to onboard new objects using only a few reference images, without requiring any CAD models or large annotated datasets. These properties make the approach well-suited for real-world industrial applications.