关键词: 小目标检测, 特征金字塔网络, 注意力机制, 特征融合

Abstract:  Tiny object detection is a huge challenge in object detection research because tiny objects take up fewer pixels in the image， which results in a lack of feature information. To address this issue， an adaptive multi-dimensional feature fusion network （AMDFF-Net） for tiny target detection is designed to improve the accuracy of tiny object detection. Firstly， by integrating pooling layers and attention mechanisms， this paper constructs a pooling attention module， enabling the model to achieve a larger receptive field to enable self-adaptive and long-range correlations in self-attention. Secondly， an adaptive selection multi-dimensional feature fusion（ASMFF） module is designed， and an adaptive multi-dimensional feature pyramid network is designed based on the ASMFF module. This network adaptively fuses image features at different scales to enhance the information about tiny objects. To verify the performance and generalization of the model， experiments are conducted on the VisDrone2019， AI-TOD， and TinyPerson datasets. The experimental results show that AMDFF-Net improves the accuracy of tiny target detection， and the effectiveness of the proposed model in tiny target detection is verified by comparing with other mainstream algorithms.

Key words:  , tiny object detection, feature pyramid network, attention mechanisms, feature fusion