Monocular Vision-Based Underwater Object Detection
AbstractIn 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chen, Z.; Zhang, Z.; Dai, F.; Bu, Y.; Wang, H. Monocular Vision-Based Underwater Object Detection. Sensors 2017, 17, 1784.
Chen Z, Zhang Z, Dai F, Bu Y, Wang H. Monocular Vision-Based Underwater Object Detection. Sensors. 2017; 17(8):1784.Chicago/Turabian Style
Chen, Zhe; Zhang, Zhen; Dai, Fengzhao; Bu, Yang; Wang, Huibin. 2017. "Monocular Vision-Based Underwater Object Detection." Sensors 17, no. 8: 1784.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.