【摘要】 随着目前目标检测任务输入图像分辨率的不断增大，在特征提取网络的感受野不变的情况下，网络提取的特征信息会越来越局限，相邻特征点之间的信息重合度也会越来越高。提出一种FSA(Fusion Self-Attention)-FPN，设计SAU(Self-Attention Upsample）模块，SAU内部结构通过CNN与自注意力机制（Self-Attention）进行交叉计算以进一步进行特征融合，并通过重构FCU(Feature Coupling Unit）消除二者之间的特征错位，弥补语义差距。以YOLOX-Darknet53为主干网络，在Pascal VOC2007数据集上进行了对比实验。实验结果表明，对比原网络的FPN，替换FSA-FPN后的平均精度值m AP@[.5:.95]提升了1.5%，预测框的位置也更为精准，在需要更高精度的检测场景下有更为出色的使用价值。更多还原

【Abstract】 With the increasing resolution of the input image of the current target detection task,the feature information extracted from the feature extraction network will become more and more limited under the condition that the receptive field of the feature extraction network remains unchanged,and the information coincidence degree between adjacent feature points will also become higher and higher.This paper proposes an FSA(fusion self-attention)-FPN,and designs SAU(self-attention upsample) module.The internal structure of SAU performs cross calculation with self-attention mechanism and CNN to further Feature fusion,and reconstructs FCU(feature coupling unit) to eliminate feature dislocation between them and bridge semantic gap. In this paper,a comparative experiment is carried out on Pascal VOC2007 data set using YOLOX-Darknet 53 as the main dry network. The experimental results show that compared with the FPN of the original network,the average accuracy of MAP@ [.5:.95] after replacing FSA-FPN is improved by 1.5%,and the position of the prediction box is also more accurate.It has better application value in detection scenarios requiring higher accuracy.更多还原