  Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the
current object detection field, which uses fully convolutional neural network
to detect all scaled objects in an image. Deconvolutional Single Shot Detector
(DSSD) is an approach which introduces more context information by adding the
deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on
PASCAL VOC2007 is improved from SSD's 77.5% to 78.6%. Although DSSD obtains
higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to
11.8. In this paper, we propose a single stage end-to-end image detection model
called ESSD to overcome this dilemma. Our solution to this problem is to
cleverly extend better context information for the shallow layers of the best
single stage (e.g. SSD) detectors. Experimental results show that our model can
reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points
respectively. Meanwhile, our testing speed is 25 FPS in Titan X GPU which is
more than double the original DSSD.
