Feature Combination Based on Receptive Fields and Cross Fusion Feature Pyramid for Object Detection
Currently, the state of the art method about object detector in image mainly depends on deep backbones, such as ResNet 50, DarkNet 53, ResNet 101 and DenseNet 169, which benefits for their powerful capability of feature representations but suffers from high computational cost. On the basis of fast lightweight backbone network (i.e. VGG 16) this paper improves the capability of feature representations by combining features of different receptive fields and cross fusing feature pyramids, and finally establishes a fast and accurate detector. The architecture of our model is designed to integrate FC CF Net with two sub modules: FC module and CF module. Inspired by the structure of receptive fields in visual systems of human, we propose a novel method about Feature Combination Based on Receptive Fields module (FC module) which takes the relationship between the size and eccentricity of receptive fields into account, and then combine them with original features for increasing the receptive field and information of the feature map. Furthermore, based on the structure of FPN (Feature Pyramid Network) we design a novel Cross Fusion Feature Pyramid module (CF module) which combines top down and bottom up connections to fuse features across scales, and achieves high level semantic feature map at all scales. Extensive experiments on PASCAL VOC 2007 and 2012 demonstrate that FC CF Net achieves state of the art detection accuracy (i.e. 82.4% mAP, 80.5% mAP) with high efficiency (i.e. 69 FPS, 35 FPS)
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