Open Access

ARTICLE

Small Object Detection via Precise Region-Based Fully Convolutional Networks

Dengyong Zhang^1,2, Jiawei Hu^1,2, Feng Li^1,2,*, Xiangling Ding³, Arun Kumar Sangaiah⁴, Victor S. Sheng⁵

1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
3 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411004, China
4 School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
5 Department of Computer Science, Texas Tech University, Lubbock, 79409, TX, USA

* Corresponding Author: Feng Li. Email:

Computers, Materials & Continua 2021, 69(2), 1503-1517. https://doi.org/10.32604/cmc.2021.017089

Received 20 January 2021; Accepted 29 March 2021; Issue published 21 July 2021

Abstract

In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps, thus preventing a loss of spatial precision. A validation experiment was conducted in which the Microsoft common objects in context (MS COCO) training dataset was oversampled. Results showed an accuracy improvement of for object detection tasks and an increase of for small objects.

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Keywords

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