Single Shot Multibox Detector With Deconvolutional Region Magnification Procedure
Yi‐Zhong Wang, Penghui Niu, Xiaoyong Guo, Guowei Yang, Jun Chen
Abstract
In this paper, we make an effort to improve the accuracy of small and medium object detections of SSD (Single Shot Multibox Detector). To this end, we introduce a deconvolutional region magnification procedure in which the existing layers in SSD play a role in the region proposal network and the proposed regions are magnified for recognition. Moreover, features are also extracted from a shallow layer and a new feature pyramid is constructed on top of these structures. Then, features are contacted and fed into classification and regression modules as in SSD. The weights of the present model are obtained via a pre-training-re-training strategy. By evaluating the model performance on a test set assembled by the samples in the PASCAL VOC and MS COCO datasets, the present model shows that the mAPs (mean average precisions) of small and medium object detections are 42.4% and 74.7% respectively, which are 27.1% and 15.6% better than SSD. This proves the effectiveness of our proposed method.