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Single-shot augmentation detector for object detection

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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.

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Acknowledgements

This project was partially supported by Grants from Natural Science Foundation of China 71671178, 9154620 and 61202321 and the Open Project of the Key Lab of Big Data Mining and Knowledge Management. It was also supported by Hainan Provincial Department of Science and Technology under Grant No. ZDKJ2016021 and by Guangdong Provincial Science and Technology Project 2016B010127004.

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Jiaxu Leng

  2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

    Ying Liu

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  1. Jiaxu Leng
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  2. Ying Liu
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Correspondence to Jiaxu Leng.

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Leng, J., Liu, Y. Single-shot augmentation detector for object detection. Neural Comput & Applic 33, 3583–3596 (2021). https://doi.org/10.1007/s00521-020-05202-0

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