Dual attention based feature pyramid network

Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network (DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the top-down pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network (FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.