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Proceedings Paper

Extend the shallow part of single shot multibox detector via convolutional neural network
Author(s): Liwen Zheng; Canmiao Fu; Yong Zhao
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Paper Abstract

Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current object detection field, which uses fully convolutional neural network to detect all scaled objects in an image. Deconvolutional Single Shot Detector (DSSD) is an approach which introduces more context information by adding the deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on PASCAL VOC2007 is improved from SSD’s 77.5% to 78.6%. Although DSSD obtains higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to 11.8. In this paper, we propose a single stage end-to-end image detection model called ESSD to overcome this dilemma. Our solution to this problem is to cleverly extend better context information for the shallow layers of the best single stage (e.g. SSD) detectors. Experimental results show that our model can reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points respectively. For 300×300 input, our testing speed is 25 FPS in single Nvidia Titan X GPU which is more than the original execution speed of DSSD.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080613 (9 August 2018); doi: 10.1117/12.2503001
Show Author Affiliations
Liwen Zheng, Peking Univ. Shenzhen Graduate School (China)
Canmiao Fu, Peking Univ. Shenzhen Graduate School (China)
Yong Zhao, Peking Univ. Shenzhen Graduate School (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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