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The accuracy of the object detection model depends on whether the anchorboxes effectively trained. Because of the small number of GT boxes or objecttarget is invariant in the training phase, cannot effectively train anchorboxes. Improving detection accuracy by extending the dataset is an effectiveway. We propose a data enhancement method based on the foreground-backgroundseparation model. While this model uses a binary image of object target randomperturb original dataset image. Perturbation methods include changing the colorchannel of the object, adding salt noise to the object, and enhancing contrast.The main contribution of this paper is to propose a data enhancement methodbased on GAN and improve detection accuracy of DSSD. Results are shown on bothPASCAL VOC2007 and PASCAL VOC2012 dataset. Our model with 321x321 inputachieves 78.7% mAP on the VOC2007 test, 76.6% mAP on the VOC2012 test.

Improve Object Detection by Data Enhancement based on Generative Adversarial Nets