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Few-Shot Target Detection Algorithm Based on Adaptive Sampling Meta-DETR
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DOI：10.3390/electronics14173506.png)
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Abstract
En 中文
Meta-DETR is a few-shot target detection algorithm that combines meta-learning and transformer architecture to solve the problem of data sample scarcity. This algorithm uses deformable attention to focus feature learning process more accurately on the target and its surroundings. However, the number of sampling points in the deformable attention is fixed, which limits the effective information involved in feature extraction, resulting in insufficient feature extraction of the target and affecting detection performance. To solve this problem, a Meta-DETR few-shot target detection algorithm based on adaptive sampling deformable attention is proposed. Firstly, the cosine similarity between feature points is calculated by query features that are integrated with support features. Secondly, the number of related features of each feature point is counted by the similarity threshold. Thirdly, the final number of sampling points of the feature map are calculated by using the idea of maximum inter-class variance to achieve adaptive sampling. Finally, adaptive sampling deformable attention is integrated into Meta-DETR to achieve few-shot target detection. From the attention activation map, it can be seen that the deformable attention based on adaptive sampling pays more attention to the target itself. Compared with Meta-DETR, the proposed algorithm improves the detection accuracy of novel classes by 0.9%, 0.7%, 1.4%, and 2.1%, respectively, for shots 1, 2, 3, and 10 in partition 1 on the PASCAL VOC dataset; 3.5%, 0.1%, 5.5%, and 5.7%, respectively, for shots 2, 3, 5, and 10 in partition 2; and 1.9%, 1.0%, 2.1%, and 0.1%, respectively, for shots 2, 3, 5, and 10 in partition 3. Compared with MPF-Net, CRK-Net, and FSCE, the proposed algorithm achieves the best performance and can effectively realize detection under few-shot conditions. In addition, experiments on a self-made infrared dataset further validate the effectiveness of the algorithm proposed in this paper.
Keywords:
Meta-DETR
few-shot target detection
adaptive sampling
deformable attention
transformer architecture
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