文献类型：期刊文献

英文题名：Object Detection in Aerial Images Using Feature Fusion Deep Networks

作者：Long, Hao[1,2];Chung, Yinung[2];Liu, Zhenbao[3];Bu, Shuhui[3]

第一作者：龙浩;Long, Hao

通讯作者：Chung, YN[1];Liu, ZB[2]

机构：[1]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]Natl Changhua Univ Educ, Dept Elect Engn, Changhua 50007, Taiwan;[3]Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China

第一机构：北京联合大学机器人学院

通讯机构：[1]corresponding author), Natl Changhua Univ Educ, Dept Elect Engn, Changhua 50007, Taiwan;[2]corresponding author), Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China.

年份：2019

卷号：7

起止页码：30980-30990

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20191706836049);Scopus(收录号：2-s2.0-85064651668);WOS:【SCI-EXPANDED(收录号:WOS:000462849800001)】；

基金：This work was supported in part by the Natural Science Foundation of China under Grant 61672430, in part by the Shaanxi Key Research and Development Program under Grant S2019-YF-ZDCXL-ZDLGY-0227, in part by the Aeronautical Science Fund under Grant BK1829-02-3009, in part by the NWPU Basic Research Fund under Grant 3102018jcc001, in part by the Science and Technique Program of Beijing Municipal Education Commission under Grant KM201711417009, and in part by the Ministry of Science and Technology under Grant MOST 105-2221-E-018-023.

语种：英文

外文关键词：Convolutional neural networks (CNNs); aerial images; feature fusion deep networks (FFDN); object detection

摘要：Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.