An Interconnected Feature Pyramid Networks for object detection

Although deep learning makes major breakthroughs in object detection, object detection still faces several limitations listed as follows: (1) Many works underplay the feature selection, leading to the resulting key features are not prominent enough and prone to noise; (2) Many works pass back features in a layer-by-layer manner to achieve multi-scale features. However, as the distance of layers from each other increases, the semantics are diluted, and the transfer of information between layers becomes difficult. To overcome these problems, we propose a new Interconnected Feature Pyramid Networks (IFPN) for feature enhancement. It can simultaneously select attentive features through the attention mechanism and realize the free flow of information. On the basis of the improvements, we design a new IFPN Detector. Experiments on COCO dataset and Smart UVM dataset show that our method can bring a significant improvement.