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作 者:王洋[1] 施保昌[2] WANG Yang;SHI Baochang(Wuhan Institute of Shipbuilding Technology,Wuhan 430050,China;School of Mathematics and Statistics,Huazhong University of Science and Technology,Wuhan 430074,China)
机构地区:[1]武汉船舶职业技术学院,武汉430050 [2]华中科技大学数学与统计学院,武汉430074
出 处:《船舶》2025年第2期60-66,共7页Ship & Boat
基 金:全国船舶工业职业教育教学指导委员会和中国造船工程学会2024年度职教课题(CSNAME-ZJ-2024036)。
摘 要:水面无人艇通常工作于复杂环境,需要具备离线自主规划路线的能力。现有技术多依赖于远距离目标识别,并将光学识别作为感知手段以弥补近距离目标识别的不足。海洋环境虽变化无常,但常见的目标物体主要有礁石(水面上)、岛屿、海上生产设备(比如海上风机)和各类船舶。海洋环境采集的图像具有灰度渐变的特点,该文针对斜变换、Haar变换、斜Haar变换、Walsh-Hadamard变换、离散余弦变换和常用的差分算子等方法,在提取水上常见目标边缘上的效果及运算时间进行了理论仿真实验,根据准确性和抗噪性选择了斜Haar变换作为边缘提取算子来找出图像关注区域,接着利用Hu不变矩识别不同行驶状态下的船舶特征,为水面常见目标边缘检测与识别提供了新的技术途径。Surface unmanned vessels often operate in complex environments and require the ability to autonomously plan routes offline.Existing technologies rely heavily on long-range target recognition,while close range optical recognition can effectively serve as a perceptual tool to compensate for deficiencies.Although the marine environment is constantly changing,common target objects for recognition include reefs(on the water surface),islands,offshore production equipment(such as offshore wind turbines),and various types of ships.The images collected from marine environments have the characteristic of grayscale gradient.This article conducts theoretical simulation experiments on the effects and computation time of slant transform,Haar transform,slant Haar transform,Walsh Hadamard transform,Discrete Cosine Transform(DCT),and commonly used differential operators in extracting edges of common targets on water.Based on accuracy and noise resistance,Slant Haar transform was selected as the edge extraction operator to find the image attention area.Then use Hu invariant moments to identify the characteristics of ships in different driving states,providing a new technical approach for edge detection and recognition of common targets on water surfaces.
关 键 词:水面目标 边缘检测 斜Haar类变换 HU不变矩
分 类 号:U675.4[交通运输工程—船舶及航道工程]
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