Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images
Real-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods ar...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/10/1590 |
| _version_ | 1798004016788013056 |
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| author | Yangjie Wei Yuwei Zhang |
| author_facet | Yangjie Wei Yuwei Zhang |
| author_sort | Yangjie Wei |
| collection | DOAJ |
| description | Real-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods are comparatively low for a real USV system moving in a complicated environment. This paper proposes an efficient waterline detection method based on structure extraction and texture analysis with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of local binary patterns (LBPs) and gray level co-occurrence matrix (GLCM) were analyzed, and their advantages were integrated to calculate the texture information of river images. Then, structure extraction was introduced to preprocess the original river images so that the textures resulting from USV motion, wind, and illumination are removed. In the practical application, the waterlines of many images captured by the USV system moving along an inland river were detected with the proposed method, and the results were compared with those of edge detection and super pixel segmentation. The experimental results showed that the proposed algorithm is effective and robust. The average error of the proposed method was 1.84 pixels, and the mean square deviation was 4.57 pixels. |
| first_indexed | 2024-04-11T12:16:53Z |
| format | Article |
| id | doaj.art-2c04a8d95b1d490ab4beaaf5866f7ed7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:16:53Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2c04a8d95b1d490ab4beaaf5866f7ed72022-12-22T04:24:15ZengMDPI AGSensors1424-82202016-09-011610159010.3390/s16101590s16101590Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical ImagesYangjie Wei0Yuwei Zhang1College of Computer Science and Engineering, Northeastern University, Wenhua Str. 3, Shenyang 110819, ChinaCollege of Computer Science and Engineering, Northeastern University, Wenhua Str. 3, Shenyang 110819, ChinaReal-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods are comparatively low for a real USV system moving in a complicated environment. This paper proposes an efficient waterline detection method based on structure extraction and texture analysis with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of local binary patterns (LBPs) and gray level co-occurrence matrix (GLCM) were analyzed, and their advantages were integrated to calculate the texture information of river images. Then, structure extraction was introduced to preprocess the original river images so that the textures resulting from USV motion, wind, and illumination are removed. In the practical application, the waterlines of many images captured by the USV system moving along an inland river were detected with the proposed method, and the results were compared with those of edge detection and super pixel segmentation. The experimental results showed that the proposed algorithm is effective and robust. The average error of the proposed method was 1.84 pixels, and the mean square deviation was 4.57 pixels.http://www.mdpi.com/1424-8220/16/10/1590unmanned surface vehicle (USV)waterline detectionoptical image blurring |
| spellingShingle | Yangjie Wei Yuwei Zhang Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images Sensors unmanned surface vehicle (USV) waterline detection optical image blurring |
| title | Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images |
| title_full | Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images |
| title_fullStr | Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images |
| title_full_unstemmed | Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images |
| title_short | Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images |
| title_sort | effective waterline detection of unmanned surface vehicles based on optical images |
| topic | unmanned surface vehicle (USV) waterline detection optical image blurring |
| url | http://www.mdpi.com/1424-8220/16/10/1590 |
| work_keys_str_mv | AT yangjiewei effectivewaterlinedetectionofunmannedsurfacevehiclesbasedonopticalimages AT yuweizhang effectivewaterlinedetectionofunmannedsurfacevehiclesbasedonopticalimages |