Deep networks for ship classification based on infrared and visible images

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	Deep networks for ship classification based on infrared and visible images
	Liu TC(刘天赐)2,3,5; Shi ZL(史泽林)1,2,3,5; Wang B(王兵)4; Liu YP(刘云鹏)1,2,3,5
	2021
会议日期	2021年9月24日-26日
会议地点	长春
关键词	Ship recognition deep learning infrared images neural network
页码	1-8
英文摘要	Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-toend convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the VAIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the VAIS database.
源文献作者	中国光学工程学会
产权排序	1
会议录	智能感知与跨域协同体系研究前沿论坛会议论文集
语种	英语
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/29892]
专题	沈阳自动化研究所_光电信息技术研究室
通讯作者	Liu TC(刘天赐)
作者单位	1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China 4.AVIC Hongdu Aviation Industry Group, Jiangxi Province, Nanchang 330096, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
推荐引用方式 GB/T 7714	Liu TC,Shi ZL,Wang B,et al. Deep networks for ship classification based on infrared and visible images[C]. 见:. 长春. 2021年9月24日-26日.