Multi-Scale Receptive Field Detection Network

Abstract

Deep convolutional neural networks have contributed much to various computer vision problems including object detection. However, there are still many problems to be solved. Scale variation across object instances is one of the major challenges for object detection. In this paper, we propose a multi-scale receptive field detection network (MS-RFDN), a one-stage approach to detect objects of different scales in the image. The proposed network combines predictions of different scales from feature maps of different scales and receptive fields. To generate s scale-specific feature maps in specific layer, we design a scale-specific concatenation module (SSC module). This scale-specific feature maps are merged from the dense block and dilated block, which has the same size of the receptive field. Through our multi-scale layer network structure and scale-specific feature maps, our model has a significant improvement in small object detection. On the VOC 2007 test dataset, our method almost achieves the effect of the state-of-the-art one-stage methods, which confirmed the effectiveness of our model.

Keywords

• Object detection
• receptive field
• scale variation