郑州大学学报（理学版）2019，Vol.51Issue(3)：61-66,72,7.DOI:10.13705/j.issn.1671-6841.2018247
面向小目标检测结合特征金字塔网络的SSD改进模型
Improved SSD Model with Feature Pyramid Network for Small Object Detection
摘要
Abstract
To solve the problem that the low accuracy of SSD convolution neural network model for small target detection, an improved SSD model based on feature pyramid network was proposed. The feature pyramid network could fuse the deeper convolutional feature maps, which had more abstract and richer semantic information, and the shallower convolutional feature maps, with higher resolution and more de-tailed information. The detection process was that multi-layer feature maps obtained from the original SSD network were processed by the lateral connection layer, upsampling layer, fusion layer, and prediction layer and so on. And then the final detection results were achieved by the non-maximal suppression. In the test, PASCAL VOC 2007 and 2012 ( train+val) were used as training sets. The mAP in the PASCAL VOC 2007 (test) test set reached 75.8%, which was 1.5% higher than the original SSD model. In parti-cular, there was a 9.9% improvement in dense small-object detection of potted plants.关键词
目标检测/卷积神经网络/SSD模型/特征金字塔网络/特征图融合Key words
object detection/convolutional neural network/SSD/feature pyramid network/feature map fusion分类
信息技术与安全科学引用本文复制引用
张建明,刘煊赫,吴宏林,黄曼婷..面向小目标检测结合特征金字塔网络的SSD改进模型[J].郑州大学学报（理学版）,2019,51(3):61-66,72,7.基金项目
国家自然科学基金项目(61772454,61811530332) （61772454,61811530332）
湖南省教育厅科学研究重点项目(16A008) （16A008）
教育部高等教育司2017年第二批产学合作协同育人项目(201702137008) （201702137008）
长沙理工大学研究生课程建设项目(KC201611) （KC201611）
湖南省研究生培养创新基地项目(湘教通[2017] 451号-30). （湘教通[2017] 451号-30）
