A multiscale balanced aggregation network model for object detection

Feature Pyramid Network (FPN) is an enhancement method for CNN to express image feature. The traditional feature pyramid model cannot fully transfer shallow detail information to deep semantic features, which leads to insufficient feature fusion and has a certain impact on the learning effect of visual tasks. Regarding the above issues, this paper proposes a multi-scale balanced aggregation network model (MBA-Net). On the basis of the FPN backbone network, MBA-Net fully integrates the features of each level, promoting the full utilization of the original image information. In addition, we further enhance the feature expression ability by utilizing attention mechanism, which strengthens the expression ability of effective features by reducing the information redundancy of feature maps at different scales. Afterwards, we conduct experiments on the PASCAL VOC2012 and MS COCO2014 datasets and verifies the effectiveness of MBA-Net for feature fusion.