Open Access

ARTICLE

DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection

by Jian Peng^1,2, Yifang Zhao^1,2, Dengyong Zhang^1,2,*, Feng Li^1,2, Arun Kumar Sangaiah³

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 Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India

* Corresponding Author: Dengyong Zhang. Email:

Computers, Materials & Continua 2023, 74(2), 3405-3419. https://doi.org/10.32604/cmc.2023.027627

Received 22 January 2022; Accepted 10 May 2022; Issue published 31 October 2022

Abstract

Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive field. The method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network (RPN). In the experimental phase, we use the new strategy to extract features. The experiment takes the public dataset of Microsoft Common Objects in Context (MS COCO) object detection and the dataset of Corona Virus Disease 2019 (COVID-19) image classification as the experimental object respectively. The results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%, and small object detection in object detection tasks is improved by 4.0%.

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Keywords

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