摘要
针对无人船在城乡河网进行水上作业时规避河岸的问题,提出了一种适用于一般直线河道和弯曲河道的边界识别方法。该方法首先通过图像预处理来改善图像质量,减少噪声,然后利用Sobel边缘检测算法识别图像各区域边界,使用自适应阈值图像分割提取边界特征,对轮廓进行线性拟合,最后采用基于河岸角度的河道边界算法识别河道边界。实验结果表明,该算法能够有效的消除树木、天空造成的假边界影响,准确识别河道边界。
Aiming at the problem of avoiding the river bank when the unmanned surface vehicle carries on the water work in the urban and rural network,a suitable boundary identification method is proposed for general linear channel and tortuous channel. Firstly through the image preprocessing to improve the image quality,reduce noise,then using Sobel edge detection algorithm identify the image regional boundary,using adaptive threshold segmentation boundary feature extracting,and then contour is linear fitted,finally,the river channel boundary is marked by bank angle. Experimental results show that the proposed algorithm could effectively eliminate false boundary effects of trees and sky,which accurately recognizes river boundary.
引文
[1]罗刚,张然.无人监测船在城市内河水质监测中的应用[B].环境监控与预警,2017,9(1):18-20.
[2]王文锋,丁伟利,李勇,等.一种高效的基于平行边缘的道路识别算法[J].光学学报2015,35(7):1-7.
[3]刘媛,宋晓琳,张伟伟.基于多模型结合优化的非结构化道路检测算法[J].工程设计学报,2013,20(2):157-162.
[4]武历颖,余强.一种快速准确非结构化道路检测方法研究[J].计算机仿真,2016,33(9):174-178.
[5]刘杰.基于Snake模型的图像分割算法研究[D].重庆:重庆邮电大学,2016.
[6]刘金龙,张晓雨.基于直线模型的道路边界识别技术[J].电子科技,2014,27(10):36-39.
[7]温脂元,曹乐平.最优RGB线性组合颜色模型目标检测方法[J].计算机工程与应用,2015,51(18):218-223.
[8]王春芳.随机Hough变换实现角点检测[J].计算机工程与设计,2015,30(3):716-720.
[9]王燕清,辛柯俊,陈德运,等.基于启发式概率Hough变换的道路边缘检测方法[J].计算机科学,2013,40(9):279-287.
[10]杜高峰.基于opencv图像处理的列车受电弓动态特性检测方法研究[D].成都:西南交通大学,2015.
[11]冯阿瑞.图像归一化分割方法研究[D].重庆:重庆大学,2014.
[12]王磊,张娜,潘城.数字图像中值滤波检测[J].广东科技,2013(24):214-215.
[13]付克兰,詹旭.基于DCT的Sobel算子的图像边缘检测优化算法[J].河南科学,2015,33(12):2131-2134.
[14]刘占.基于局部期望阈值分割的图像边缘检测算法[J].计算机与现代化,2016,52(8):52-55.
[15]王燕,曹银杰,郎丰法,等.基于Emgu CV的数字相机图像采集[J].电子科技,2012,25(4):31-32.
[16]田垅,刘宗甜.最小二乘法分段直线拟合[J].计算机科学,2012,6A(39):482-484.