Supervised pyramid network based on semantic consistency for object detection

Feature pyramid network is widely used in image understanding tasks based on multi-scale feature learning. The latest multi-scale feature learning focuses on the interactive integration of features in semantic features and detail features. Feature pyramid network complements multi-scale information semantic features and detail features through feature interpolation and summation of adjacent layers. Due to the existence of nonlinear operation and convolution layers with different output dimensions, the relationship among different levels is much more complex, and pixel by pixel summation is suboptimal method. A supervised feature pyramid network based on semantic consistency for object detection is proposed. The present method is composed of asymmetric convolution lateral connection and multi-scale semantic features augmentation. The asymmetric convolution lateral connection improves the generalization of features to various pose objects by learning the feature maps of different receptive fields. The multi-scale semantic features augmentation network improves the detail expression ability of high-level features by supplementing the low-level information for the high-level feature map. Moreover, the present method can provide a better trade-off between accuracy and detection performance. Experiments conduct on the MSCOCO dataset, and the results show that the proposed object detection method's accuracy is improved by 2.6% without increasing extra FLOPs.