Object Detection Method Based on Improved Feature Pyramid Networks
To solve the problem of insufficient feature information for multi-scale targets,inspired by the BiFPN network,we proposed IN-FPN,an inverted N-type FPN structure in the Neck section of the network mod-el.In addition,the multi-scale feature fusion structure of the network was optimized,with a horizontally connect-ed hierarchical structure.The features are fused twice from top to bottom and once from bottom to top in a bidi-rectional manner,allowing shallow and deep features of the object to fully fuse and promote each other.At the same time,for the different functions of feature fusion at different scales,adaptive learning weights are added to each scale ωi.Moreover,in order to solve the problem of network degradation and improve network performance,a path containing a Block module was added based on the residual network structure.The experimental results showed:With this method,the AP values reached 53.02%and 25.21%on the COCO 2017 dataset and Vis-Drone 2019 dataset.They were both improved,compared with the benchmark model,verifying the effectiveness of the method.
NETL
NSTL
万方数据
维普
