Ship Target Detection Algorithm Based on Improved Faster R-CNN

doi:10.3390/electronics8090959

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Ship Target Detection Algorithm Based on Improved Faster R-CNN
	Qi, Liang 1; Li, Bangyu2 ; Chen, Liankai 1; Wang, Wei 1; Dong, Liang 1; Jia, Xuan 1; Huang, Jing 1; Ge, Chengwei 1; Xue, Ganmin 1; Wang, Dong 1
发表期刊	ELECTRONICS
	2019-09-01
卷号	8 期号:9 页码:19
通讯作者	Chen, Liankai(chenliankai@stu.just.edu.cn)
摘要	Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.
关键词	ship target detection Faster R-CNN scene semantic narrowing topic narrowing subnetwork
DOI	10.3390/electronics8090959
收录类别	SCI
语种	英语
资助项目	Jiangsu Collaborative Innovation Platform Project[HZ201805] ; Jiangsu Collaborative Innovation Platform Project[HZ201805]
项目资助者	Jiangsu Collaborative Innovation Platform Project
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000489128400046
出版者	MDPI
引用统计	被引频次：42[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/26448
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	Chen, Liankai
作者单位	1.Jiangsu Univ Sci & Technol, Sch Elect Informat, Ship Intelligent Mfg & Intelligent Ship Integrate, 2 Mengxi Rd, Zhenjiang 212000, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qi, Liang,Li, Bangyu,Chen, Liankai,et al. Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. ELECTRONICS,2019,8(9):19.
APA	Qi, Liang.,Li, Bangyu.,Chen, Liankai.,Wang, Wei.,Dong, Liang.,...&Wang, Dong.(2019).Ship Target Detection Algorithm Based on Improved Faster R-CNN.ELECTRONICS,8(9),19.
MLA	Qi, Liang,et al."Ship Target Detection Algorithm Based on Improved Faster R-CNN".ELECTRONICS 8.9(2019):19.