文章基本信息

标题：Small Object Detection Network Based on Feature Information Enhancement
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作者：Huilan Luo ; Pei Wang ; Hongkun Chen 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/6394823
语种：English
出版社：Hindawi Publishing Corporation
摘要：Due to the small size and weak characteristics of small objects, the performance of existing object detection algorithms for small objects is not ideal. In this paper, we propose a small object detection network based on feature information enhancement to improve the detection effect of small objects. In our method, two key modules, information enhancement module and dense atrous convolution module, are proposed to enhance the expression and discrimination ability of feature information. The detection accuracy of this method on PASCAL VOC, MS COCO, and UCAS-AOD data sets is 81.3%, 34.8%, and 87.0%, respectively. In addition, the detection results of this paper in detecting small objects are slightly (0.2% and 0.1%) higher than the current advanced algorithms (YOLOv4 and DETR, respectively). Moreover, when these two modules are integrated into other algorithms, such as RFBNet, it can also bring considerable improvement.