摘要
针对动态水下目标跟踪定位过程需要良好的实时性与鲁棒性问题,提出一种基于双目视觉的水下动态目标定位方法;利用快速引导滤波提取水下图像的光照分量,构造了一种改进的二维伽马函数,并对其参数利用光照分量的分布特性进行调整,实现了对水下不同环境图像下的亮度自适应校正处理;利用卡尔曼滤波预估下一时刻目标的位置,将预测空间作为ROI区域进行图像校正,极大降低了算法的运行时间;在HSV空间对目标进行掩膜提取,识别之后通过双目定位算法对目标进行准确定位;经过水箱试验验证,与多尺度高斯函数、双边滤波等算法相比,该方法在运行速度上有着显著的提高,达到了35FPS,在定位过程中有着较高定位精度,在方向的平均相对误差为(3.59%,3.35%,1.42%);结果表明,该算法可以满足水下动态目标跟踪定位的实时性与鲁棒性要求。
Aiming at the problem that the dynamic underwater target tracking and positioning process requires good real-time and robustness,an underwater dynamic target positioning method based on binocular vision is proposed.Fast guided filtering is used to extract the illumination component of underwater images,build the improved two-dimensional Gamma function,adjust its parameters by using the distribution characteristics of illumination components,and realize the adaptive image brightness correction under different underwater environments.Kalman filter is used to estimate the position of the target at the next moment,and taking the prediction space as the ROI region for image correction,it greatly reduces the running time of the algorithm.The target is masked in the HSV space,and it is accurately located by the binocular localization algorithm after recognition.Through the water tank testing,compared with multi-scale Gaussian function,bilateral filtering and other algorithms,the proposed method has a significant improvement in running speed,reaching 35 FPS,and it has a high positioning accuracy during the positioning process,with the average relative errors of 3.59%,3.35%,1.42%in the X,Y and Z directions.The results show that the algorithm can meet the real-time and robustness requirements in underwater dynamic target tracking and positioning.
作者
柳靖彬
刘卫东
李乐
张帅军
张文博
李艳丽
LIU Jingbin;LIU Weidong;LI Le;ZHANG Shuaijun;ZHANG Wenbo;LI Yanli(School of Marine Science and Technology,Northwestern Polytechnical University,Xi'an 710072,China)
出处
《计算机测量与控制》
2024年第3期306-312,共7页
Computer Measurement &Control
基金
国家自然科学基金(61903304)
中央高校基本科研业务费项目(3102020HHZY030010)
“111”引智计划项目(B18041.0)。
关键词
图像增强
二维伽马函数
快速引导滤波
卡尔曼滤波
双目视觉
image enhancement
two-dimensional Gamma function
fast guided image filter
Kalman filter
binocular vision
