Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation

In this paper, we propose an improved Region Proposal Network (RPN) by introducing a metric-based nonlinear classifier to compute the similarity between features extracted from the backbone network and those of new classes. This enhancement aims to improve the detection precision for candidate boxes of new classes and filter out candidate boxes with high Intersection of Union (IoU). Simultaneously, we introduce an attention-based Feature Aggregation Module (AFM) in Region of Interest (RoI) Align to aggregate feature information from different levels, obtaining more comprehensive information and feature representation to address the issue of missing feature information due to scale differences. Combining these two improvements, we present a novel few-shot object detection algorithm—IFA-FSOD. We conduct extensive experiments on datasets. Compared to some mainstream few-shot object detection algorithms, the IFA-FSOD algorithm can select more accurate candidate boxes, addressing issues of missed high IoU candidate boxes and incomplete feature information capture, resulting in higher precision.