Few-Shot Detection Based on an Enhanced Prototype for Outdoor Small Forbidden Objects

In this paper, we propose an enhanced prototype based on a regional many-to-many attention mechanism for few-shot object detection of forbidden objects such as knives and sticks. Specifically, First, we use the original prototype to obtain the invariance of the image to better represent the invariant features of images. Then, we use the enhanced prototype to weight the support features of different query images of knives and sticks to avoid over-fitting. Finally, we use a joint regional consistency loss function to balance and maximize the consistency between the enhanced prototype and the original prototype, which facilitates online learning of invariant object features and improves the efficiency of object detection. The results of experiment show that the enhanced prototype can effectively detect knives and sticks, compared with state-of-art methods. Our method achieves significant improvements in both visual and quantitative evaluation metrics.