A Novel Pyramid Network with Feature Fusion and Disentanglement for Object Detection

In order to alleviate the scale variation problem in object detection, many feature pyramid networks are developed. In this paper, we rethink the issues existing in current methods and design a more effective module for feature fusion, called multiflow feature fusion module (MF(3)M). We first construct gate modules and multiple information flows in MF(3)M to avoid information redundancy and enhance the completeness and accuracy of information transfer between feature maps. Furtherore, in order to reduce the discrepancy of classification and regression in object detection, a modified deformable convolution which is termed task adaptive convolution (TaConv) is proposed in this study. Different offsets and masks are predicted to achieve the disentanglement of features for classification and regression in TaConv. By integrating the above two designs, we build a novel feature pyramid network with feature fusion and disentanglement (FFAD) which can mitigate the scale misalignment and task misalignment simultaneously. Experimental results show that FFAD can boost the performance in most models.