Few-Shot Object Detection on Remote Sensing Images Based on Feature Weighting and Fusion
Object detectors based on convolutional neural networks require a large number of labeled samples for training.To address the is-sue of poor generalization of the object detector due to insufficient training samples,this paper proposes a few-shot object detection method on remote sensing images via feature weighting and fusion based on meta-feature modulation.Firstly,the feature learning module with bottleneck structure(C3)is embedded in the meta-feature extraction network to increase network depth and receptive field.Secondly,the path aggrega-tion network(PAN)are used for meta-feature fusion,which effectively enhance the perception of the network to multi-scale remote sensing objects.Then,prototype vectors are learned from a lightweight convolutional neural network for meta-feature weighting,which transfers model knowledge from the base class to the new class and makes the model lightweight at the same time.Experimental results show that on the NWPU VHR-10 and DIOR datasets,the proposed method improves the mean average precision on the new class of remote sensing objects by 29.40%and 11.78%,respectively,compared to FSODM method.Moreover,visualization results demonstrate that this method performs better on few-shot remote sensing object detection.
万方数据
维普
