Bidirectional Pathway Feature Pyramid Networks and Reverse Scale-Transfer Layer for Detecting Mult-Scale Ships
Guanhua Jiang, Yanan You, Gang Meng, Bohao Ran, Fang Liu · 2021
Multi-scale ship detection for remote sensing images is always a popular research field in civil and military application. In this paper, in order to solve the feature information single pathway flow in feature layer causes the lack of detailed information in the deep feature layer, we propose a bidirectional pathway feature pyramid networks (BP-FPN) method, which enables the deep feature layer to have strong semantic information as well as rich detailed information. At the same time, the Reverse Scale-transfer Layer down-sampling method is proposed to reduce the information loss of feature layer in the process of downsampling. It ensures that the feature layer maintain information during the down-sampling process. Experimental results on a dataset collected from Google Earth have quantitatively and qualitatively demonstrated the effectiveness of our approach