  The accuracy of the object detection model depends on whether the anchor
boxes effectively trained. Because of the small number of GT boxes or object
target is invariant in the training phase, cannot effectively train anchor
boxes. Improving detection accuracy by extending the dataset is an effective
way. We propose a data enhancement method based on the foreground-background
separation model. While this model uses a binary image of object target random
perturb original dataset image. Perturbation methods include changing the color
channel of the object, adding salt noise to the object, and enhancing contrast.
The main contribution of this paper is to propose a data enhancement method
based on GAN and improve detection accuracy of DSSD. Results are shown on both
PASCAL VOC2007 and PASCAL VOC2012 dataset. Our model with 321x321 input
achieves 78.7% mAP on the VOC2007 test, 76.6% mAP on the VOC2012 test.
