1 research outputs found
A Single-shot Object Detector with Feature Aggragation and Enhancement
For many real applications, it is equally important to detect objects
accurately and quickly. In this paper, we propose an accurate and efficient
single shot object detector with feature aggregation and enhancement (FAENet).
Our motivation is to enhance and exploit the shallow and deep feature maps of
the whole network simultaneously. To achieve it we introduce a pair of novel
feature aggregation modules and two feature enhancement blocks, and integrate
them into the original structure of SSD. Extensive experiments on both the
PASCAL VOC and MS COCO datasets demonstrate that the proposed method achieves
much higher accuracy than SSD. In addition, our method performs better than the
state-of-the-art one-stage detector RefineDet on small objects and can run at a
faster speed