Abstract: Feature pyramid network (FPN) is an enhanced method for CNN network to express and output image information. It has been widely used in object detection network and has achieved significant effect improvement. The traditional feature pyramid model can not fully transfer the shallow details to the deep semantic features, which leads to inadequate feature fusion. It can only rely on the deep semantic information to make predictions, but ignore the underlying location information of the network. In terms of the above problems, we proposed a muti-scale feature fusion network based on feature pyramid model. Based on the FPN backbone, a mixed feature pyramid and a pyramid fusion module are designed. Based on the attention mechanism, multi-scale deep fusion of the feature pyramid is performed. We carry out the experiments on the PASCAL VOC2012 and MS COCO2014 datasets, and verify the effectiveness of MSFFN for feature fusion.

Key words: feature pyramid model, muti-scale feature fusion network, attention mechanism