基于全景视觉的无人船水面障碍物检测方法
Detection Method of Water-Surface Obstacles for Unmanned Ships Based on Panoramic Vision
周金涛 1高迪驹 1刘志全1
作者信息
- 1. 上海海事大学航运技术与控制工程交通运输行业重点实验室,上海 201306
- 折叠
摘要
无人船航行时水面障碍物检测因视角不足,导致漏检或误检,同时为满足无人船安全正常作业的需求,提出基于全景视觉的无人船水面障碍物目标检测方法.与传统的单目和双目视觉相比,全景视觉具有水平方向大视场监控的优点.基于多目全景视觉系统获得待拼接图像,在加速稳健特征(SURF)算法的基础上进行图像配准,引入k维树来构建数据索引,实现搜索空间级分类并进行快速匹配.通过M估计样本一致算法对匹配点进行优化,剔除误匹配点.对于图像融合中重叠区域出现的拼接缝隙或重影问题,设计一种基于圆弧函数的加权融合算法.提出改进的水面障碍物目标检测模型DS-YOLOv5s,将拼接好的全景图像作为训练好的模型作为输入,从而检测目标障碍物.实验结果表明,改进后的SURF算法与SURF算法相比特征点的匹配正确率提高11.47个百分点,在匹配时间上比SURF、RANSAC算法缩短5.83 s,DS-YOLOv5s模型的mAP@0.5达到95.7%,检测速度为51 帧/s,符合实时目标检测标准.
Abstract
During the navigation of an unmanned ship,an inadequate perspective may cause obstacles on the water-surface to be missed.To enable the safe and normal operation of unmanned ships,this study proposes a panoramic vision-based method for the detection of water-surface obstacles.Panoramic vision is employed because it has the advantage of horizontal large-field monitoring,in contrast to traditional monocular and binocular vision.In the proposed approach,a multi-camera panoramic vision system acquires an image to be stitched.The Speeded-Up Robust Feature(SURF)algorithm then performs image registration.A k-dimensional tree constructs a data index,facilitating search-space level classification and fast matching.The M-estimation-based sample consensus algorithm optimizes the matching points and eliminates the mismatched points.A specifically designed arc function-based weighted fusion algorithm stitches the gaps and overcomes ghosting in the overlapping areas during image fusion.Finally,this study proposes an improved water-surface obstacle target detection model DS-YOLOv5s.This model takes the stitched panoramic image as input to detect target obstacles.In experiment,the improved SURF algorithm improved the feature-point matching accuracy by 11.47 percentage points compared to the SURF algorithm,and shorten the matching time by 5.83 seconds compared to the SURF,RANSAC algorithm.The DS-YOLOv5s model mAP@0.5 reached 95.7%,with a detection speed of 51 frames/s,conforming to real-time object detection standards.
关键词
全景视觉/图像拼接/无人船/改进YOLOv5/目标检测Key words
panoramic vision/image stitching/unmanned ship/improved YOLOv5/target detection引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据