# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 5 Conclusions and Further Work  
In this paper, we propose a novel few-shot object detection model based on improved RPN and feature aggregation (IFA-FSOD). By designing an RPN with a metric-based nonlinear classifier, the detection precision of new class candidate boxes can be improved to screen for high IoU candidate boxes. We introduce an attention-based feature aggregation module (AFM) in RoI Align to aggregate feature information from different levels, obtaining more comprehensive and important information and feature representations, and solving the problem of missing feature information. The ablation experiment demonstrated the effectiveness of each component in IFA-FSOD. Our proposed IFA-FSOD has been experimentally proven to have certain object detection capabilities in few-shot sizes on two benchmark datasets, namely PASCAL VOC and MSCOCO. In general, IFA-FSOD is innovative and potential in improving the detection performance of few-shot object detection. IFA-FSOD proposes innovative ideas in target detection performance, especially in processing small sample data and enhancing feature representation. However, any model has its limitations, and IFA-FSOD is no exception. The main limitations of this method and model are as follows: (1) The introduction of a metric-based nonlinear classifier may increase the computational complexity of RPN, especially when dealing with a large number of candidate boxes. This may increase the consumption of computing resources in the training and reasoning phase of the model, and affect the real-time performance. (2) The AFM module enhances feature representation by aggregating feature information from different levels, but this will also increase the amount of computation and memory consumption. Especially in deep networks, the size and number of channels of the feature graph are usually large, which further aggravates the problem of resource consumption. (3) Although IFA-FSOD aims to improve the performance of small sample target detection, its generalization ability may be limited. Especially when faced with new categories or complex scenes that are quite different from the training data, the model may not be effectively generalized. (4) Although IFA-FSOD aims to deal with small sample data, the model still needs a certain amount of labeled data to train. For some fields or tasks, high-quality annotation data may be difficult to obtain or expensive. In future research, we will deeply study the dense relationship between similar and different features to learn a more adaptive feature model so as to further improve the performance of small sample target detector. For example, the same and different features are fused to form a richer feature representation. The feature fusion technology in deep learning is used to fuse features of different scales and levels to improve the robustness and generalization ability of the feature model. Adaptive learning algorithms (such as online learning, incremental learning, etc.) are used to update the feature model according to the new samples, so that it can adapt to the change in target detection task. The knowledge learned from large-scale datasets is migrated to small sample target detection tasks by  
Figure 11: IFA-fosd can completely detect the incompleteness of the detected target object in the base class. transfer learning and other methods to improve the performance of the detector. According to the analysis results, the feature model and detector are optimized and improved. Conceptualization, G.W. ; methodology, Q.P. ; software, K.F. ; validation, Q.P. ; investigation, K.F. ; writing--original draft preparation, K.F. ; writing--review and editing, Q.P. ; visualization, G.W. ; supervision, G.W. ; project administration, G.W. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China. No. 62062007. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.