东南大学学报（英文版）2020，Vol.36Issue(3)：252-263,12.DOI:10.3969/j.issn.1003-7985.2020.03.002
用于目标检测的邻域融合与分层并行特征金字塔网络
Neighborhood fusion-based hierarchical parallel feature pyramid network for object detection
摘要
Abstract
In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network (NFPN) is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network (FPN) and deconvolutional single shot detector (DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4％ to 5％ higher mAP (mean average precision) than SSD,and 2％ to 3％ higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300 ×300 input reaches 79.4％ mAP at 34.6 frame/s,and the mAP can raise to 82.9％ after using the multi-scale testing strategy.关键词
计算机视觉/深度卷积神经网络/目标检测/分层并行/特征金字塔网络/多尺度特征融合Key words
computer vision/deep convolutional neural network/object detection/hierarchical parallel/feature pyramid network/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
莫凌飞,胡书铭..用于目标检测的邻域融合与分层并行特征金字塔网络[J].东南大学学报（英文版）,2020,36(3):252-263,12.基金项目
The National Natural Science Foundation of China (No.61603091). （No.61603091）
