Yuejun Liu, Liyong Ma*, Wei Xie, Xiaolei Zhang and Yong Zhang Pages 1145 - 1152 ( 8 )
Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV.
Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection.
Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas.
Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.
Gray level co-occurrence matrix, image processing, texture entropy, unmanned surface vehicles (USV), water boundary line detection, canny and hough transform.
School of Automation, Harbin University of Science and Technology, Harbin, School of Information Science and Engineering, Harbin Institute of Technology, Weihai, School of Information Science and Engineering, Harbin Institute of Technology, Weihai, School of New Energy, Harbin Institute of Technology, Weihai, Department of Civil Engineering, Harbin Institute of Technology, Weihai