SSFENet: Spatial and Semantic Feature Enhancement Network for Object Detection

Current state-of-the-art object detectors generally use pre-trained classification networks to extract features, and then utilize feature pyramids to detect objects of different scales. However, classification networks prefer translation invariance and ignore the location information, so directly using the extracted features for fusion will affect the performance. In this paper, we present a novel network to address this dilemma, denoted as Spatial and Semantic Feature Enhancement Network (SSFENet). First, we introduce Spatial Feature Enhancement Block to utilize dilated convolution and weighted feature fusion to enhance the spatial information in features. Second, in the low-level stage, our Semantic Feature Enhancement Block uses the backbone network of the high-level stage to obtain features with richer semantic information and only introduces little computational cost due to the use of shared convolution layers. Experimental results on the MS-COCO benchmark show that the proposed SSFENet significantly improves the mAP of commonly used object detectors.