文章基本信息

标题：ブースティングによる機械学習に基づく海底物体の検出
本地全文：下载
作者：丹康弘 ; タンジュークイ ; 金亨燮等
期刊名称：日本船舶海洋工学会論文集
印刷版ISSN：1880-3717
电子版ISSN：1881-1760
出版年度：2013
卷号：18
页码：115-121
DOI：10.2534/jjasnaoe.18.115
语种：Japanese
出版社：社団法人日本船舶海洋工学会
摘要：Side-scan and forward looking sonars are some of the most widely used imaging systems for obtaining large scale images of a seafloor, and their use continues to expand rapidly with their increasing deployment on Autonomous Underwater Vehicles. However,it is difficult to extract quantitative information from the images generated from these processes, in particular, for the detection and extraction of information on the objects within these images. We propose in this paper an algorithm for automatic detection of underwater objects in side-scan images based on machine learning employing adaptive boosting. Experimental results show that the method produces consistent maps of a seafloor.