Accurate and Efficient Object Detection with Context Enhancement Block

Yuhao Chen, Min Zhao, Xin Tan, Hong Tang, Dihua Sun

Abstract: Recently feature pyramid composed of multi-level feature maps has been extensively used in region-free detectors to address multi-scale object detection. However, the contradiction between scale and context in the feature pyramid limits the detection performance, extraordinarily on small objects. Most works introduce an extra top-down path to overcome the limitation yet suffering from high computational burden. In this paper, we propose a novel Expansion Receptive Field Block (ERFB) to capture multiple strong contextual features at low computational cost, and then apply the Feature Attention Block (FAB) to eliminate the inconsistency between different features to generate more discriminative features. To be further, we construct an efficient and accurate detector (named CEBNet) mainly consists of Context Enhancement Blocks (CEBs), which are cascaded with ERFB and FAB. The extensive experiments on Pascal VOC and MS COCO demonstrate that CEBNet achieves state-of-the-art detection accuracy at a real-time processing speed.