Single-shot augmentation detector for object detection

Abstract

Single-shot multibox detector (SSD), one of the top-performing object detection algorithms, has achieved both high accuracy and fast speed. However, its performance is limited by two factors: (1) anchors are generated uniformly over the image by predefined manners, and (2) multiscale features from the feature pyramid are used to detect objects independently. In this paper, we propose a single-shot augmentation detector, called SSADet, that significantly improves the detection accuracy of the original SSD with a slight decrease in speed. SSADet mainly consists of two modules, namely the anchor prediction module and the feature fusion module. These two modules aim to generate anchors with any scale and aspect ratio and fuse multiscale features from different layers, respectively. Specifically, we define an anchor generator whose parameter weights are predicted dynamically by a small neural network and then use the anchor generator to generate optimal anchors over the image in the anchor prediction module. In the feature fusion module, multiscale features from the feature pyramid are concatenated to generate a new feature pyramid through a set of downsampling and upsampling operations. The new feature pyramid takes the generated anchors as the input from the anchor prediction module to predict the final detection results. Extensive experiments are conducted to demonstrate the effectiveness of SSADet on the PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO detection datasets. The experimental results show that SSADet achieves state-of-the-art detection performance with high efficiency.