Dense Receptive Field for Object Detection

Current one-stage single-shot detectors such as DSSD and StairNet based on aggregating context information from multiple scales have shown promising accuracy. However, existing multi-scale context fusion techniques are insufficient for detecting objects of different scales. In this paper, we investigate how to detect different objects with different scales with respect to accuracy-vs-speed trade-off. We propose a novel single-shot based detector, called DRFNet which fuses feature maps with different sizes of the receptive field to boost the detection accuracy. Our final model DRFNet detector unifies comprehensive context information from various receptive fields effectively to enable it to detect objects in different sizes with higher accuracy. Experimental results on PASCAL VOC 2007 benchmark (79.6% mAP, 68 FPS) demonstrate that DRFNet is better than other state-of-the-art one-stage detectors similar to FPN. Code is released at https://github.com/yqyao/DRFNet.