IAFPN: interlayer enhancement and multilayer fusion network for object detection

Abstract

Feature pyramid network (FPN) improves object detection performance by means of top-down multilevel feature fusion. However, the current FPN-based methods have not effectively utilized the interlayer features to suppress the aliasing effects in the feature downward fusion process. We propose an interlayer attention feature pyramid network that attempts to integrate attention gates into FPN through interlayer enhancement to establish the correlation between context and model, thereby highlighting the salient region of each layer and suppressing the aliasing effects. Moreover, in order to avoid feature dilution in the feature downward fusion process and inability of multilayer features to utilize each other, simplified non-local algorithm is used in the multilayer fusion module to fuse and enhance the multiscale features. A comprehensive analysis of MS COCO and PASCAL VOC benchmarks demonstrate that our network achieves precise object localization and also outperforms current FPN-based object detection algorithms.