Monocular Vision-Based Underwater Object Detection

CORC > 上海光学精密机械研究所 > 中国科学院上海光学精密机械研究所 > 信息光学与光电技术实验室

	Monocular Vision-Based Underwater Object Detection
	Dai, Fengzhao; Zhang, Zhen; Bu, Yang; Wang, Huibin; Chen, Zhe
刊名	Sensors
	2017
卷号	17 期号:8
通讯作者	chenzhe@hhu.edu.cn ; zz_hhuc@hhu.edu.cn ; fzdai@siom.ac.cn ; buyang@siom.ac.cn ; hbwang@hhu.edu.cn
英文摘要	In this paper, we propose an underwater object detection method using monocular vision sensors. In addition to commonly used visual features such as color and intensity, we investigate the potential of underwater object detection using light transmission information. The global contrast of various features is used to initially identify the region of interest (ROI), which is then filtered by the image segmentation method, producing the final underwater object detection results. We test the performance of our method with diverse underwater datasets. Samples of the datasets are acquired by a monocular camera with different qualities (such as resolution and focal length) and setups (viewing distance, viewing angle, and optical environment). It is demonstrated that our ROI detection method is necessary and can largely remove the background noise and significantly increase the accuracy of our underwater object detection method.
收录类别	SCI
资助信息	National Natural Science Foundation of China [61501173, 61671201]; Natural Science Foundation of Jiangsu Province [BK20150824]; Fundamental Research Funds for the Central Universities [2017B01914]; Jiangsu Overseas Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents
WOS记录号	WOS:000408576900087
内容类型	期刊论文
源URL	[http://ir.siom.ac.cn/handle/181231/28508]
专题	上海光学精密机械研究所_信息光学与光电技术实验室
作者单位	中国科学院上海光学精密机械研究所
推荐引用方式 GB/T 7714	Dai, Fengzhao,Zhang, Zhen,Bu, Yang,et al. Monocular Vision-Based Underwater Object Detection[J]. Sensors,2017,17(8).
APA	Dai, Fengzhao,Zhang, Zhen,Bu, Yang,Wang, Huibin,&Chen, Zhe.(2017).Monocular Vision-Based Underwater Object Detection.Sensors,17(8).
MLA	Dai, Fengzhao,et al."Monocular Vision-Based Underwater Object Detection".Sensors 17.8(2017).