Receptive Field Pyramid Network for Object Detection

Current state-of-the-art methods usually utilize feature pyramid to provide various receptive fields for detecting objects at different scales. However, the feature maps from low- to high-level layers have large semantic gaps and are with different spatial resolutions, so that their representational capacity differs and noise is introduced when fusing them. To overcome this limitation and carry out better object detection, we design a novel network named Receptive Field Pyramid Network (RFPN). The proposed method is derived based on a receptive field pyramid through dilated convolutions, such that all of the extracted feature maps are with strong semantics and the same resolution. Moreover, we propose a pyramid attention mechanism by iteratively leveraging information from previous receptive fields to give higher responses for objects of interest. Experimental results on publicly available datasets show that the proposed method achieves better results than existing methods for comparison.