A Non-Local Attention Feature Fusion Network for Multiscale Object Detection

Feature pyramid networks (FPN) provide typical architectures for building learning networks with advanced semantic features, which are essential for object recognition at different scales. However, FPN have severe shortcomings in the feature extraction and fusion stages, such as making the extracted features lack of contextual and deep semantic information. In this work, we propose a non-local channel and spatial attention feature pyramid network (NCS-FPN) to improve multi-scale learning. In the feature extraction process, contextual semantic information from different scales is collected through non-local attention networks. In the feature fusion phase, deeper feature information from both spatial and context-aware sources is aggregated to enhance multi-scale feature extraction. Extensive experiments are carried out on two public datasets, MS COCO and PASCAL VOC, are carried out and the results demonstrate that NCS-FPN achieves better performance than several SOTA methods in most cases.