Improved Few-Shot Object Detection Method Based on Faster R-CNN

Uneven distribution of object features and insufficient feature learning significantly affect the accuracy and generalizability of existing detection methods. This paper proposes an improved two-stage few-shot object detection method that builds upon the faster region-based convolutional neural network framework to enhance its performance in detecting objects with limited training data. First, a modified data augmentation method for optical images is introduced, and a Gaussian optimization module of sample feature distribution is constructed to enhance the model's generalizability. Second, a parameter-less 3D space attention module without additional parameters, is added to enhance the space features of a sample, where a neuron linear separability measurement and feature optimization module based on mathematical operations are used to adjust the feature distribution and reduce data distribution bias. Finally, a class feature vector extractor based on meta-learning is provided to reconstruct the feature map by overlaying a class feature vector from the target domain onto the query image. This process improves accuracy and generalization performance, and multiple experiments on the PASCAL VOC dataset show that the proposed method has higher detection accuracy and stronger generalizability than other methods. Especially, the experiment using practical images under complicated environments indicates its potential effectiveness in real-world scenarios.