Multi-layers context convolutional neural network for object detection

被引：0
|
作者：
Wang H. [1 ]
Shan W. [1 ]
Fang B. [1 ]
机构：
[1] School of Computer Science and Information Engineering, He-fei University of Technology, Hefei
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Feature Fusion; Multi-layers Context Information(MLC); Object Detection; Region Proposal Network(RPN);
D O I：
10.16451/j.cnki.issn1003-6059.202002003
中图分类号：
学科分类号：
摘要：
Insufficient feature information in object detection results in low accuracy of small targets and occluded targets detection. Therefore, multi-layers context convolutional neural network(MLC-CNN) is proposed, and contextual information of multiple layers is extracted to combine local features of objects in object detection. MLC-CNN consists of region proposal network(RPN) sub-network and multi-layers context(MLC) sub-network. RPN sub-network is employed to capture feature vectors with the fixed length as object features, and MLC is employed to obtain the corresponding contextual information of the different feature maps. Finally, two kinds of information are fused. In addition, hard example training is employed to solve the problem of imbalance data. Experiments on PASCAL VOC2007 and PASCAL VOC2012 datasets indicate that mean average precision(mAP) value is improved. © 2020, Science Press. All right reserved.
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页码：113 / 120
页数：7
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