Multi-scale redistribution feature pyramid for object detection

Abstract

Many feature pyramid models now use simple contextual feature aggregation, which does not make full use of the semantic information of multi-scale features. Therefore, Multi-scale Redistribution Feature Pyramid Network (MRFPN) is proposed. In order to strengthen feature fusion and solve the two problems of feature redundancy and high abstraction, modified-BiFPN is designed. The features output by the modified-BiFPN module are semantically balanced through the balanced feature map, so as to alleviate the semantic differences between multi-scales. Then a new channel attention module is proposed, which realizes the multi-scale association of the feature information fused to the balanced feature map. Finally, a new feature pyramid is formed through the residual edge for prediction. MRFPN have been evaluated on PASCAL VOC 2012 dataset and MS COCO dataset, which has higher detection accuracy compared with other state-of-the-art detectors.