可见光海面目标检测的结构随机森林方法

由于受到海岸景物和海面波纹等复杂海况的影响,基于可见光海面图像目标检测是一个技术难点.本文提出一个结构随机森林检测海面目标的方法.该方法首先基于图像块构建随机森林,然后将结构学习策略用于随机森林的预测输出空间,在样本空间训练随机森林,最后通过随机森林将图像块分类为海面图像的目标区域与背景区域.实验结果表明相对Canny 算子,Threshold-Segment 算子,Salience_ROI 算子,文中方法在海面图像目标检测中取得了更高的检测率,且计算开销较小.

Abstract

For the influence of some complex sea states such as coastal scenery and surface ripple in sea images,target detection based on the visible light image is a technical difficult problem of the current.This paper presents a method of structured random forests for target detection in sea images.The method first constructs random decision forest based on image block,applies structured learning strategy to the forecast output spatial of the constructed random decision forest,and then trains the random decision forest in the sample space,and finally classifies the testing image blocks as the target region and the background region through random decision forest.The experimental results show that compared with the Canny operator,the Threshold-Segment operator,and the Salience_ROI operator,the method of this paper has significant advantages in the aspects of sea image target detection and uses low computation cost.

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雷琴, 施朝健, 陈婷婷. 可见光海面目标检测的结构随机森林方法[J]. 光电工程, 2015, 42(7): 31. LEI Qin, SHI Chaojian, CHEN Tingting. Structured Random Forests for Target Detection in Sea Images[J]. Opto-Electronic Engineering, 2015, 42(7): 31.

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