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暗光环境下机械结构边缘检测算法的研究

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针对暗光环境下机械结构边缘检测精度低、效果不理想的问题,提出了一种基于多步骤图像增强和自适应形态学操作的物体边缘检测方法.该方法利用多尺度Retinex算法增强暗光图像的亮度和对比度,结合锐化、滤波算法去噪并保留细节,同时采用自适应形态学操作剔除伪边缘和强化弱边缘.所提出的方法在暗光条件下的物体边缘检测实验中成功率达86.7%,比传统方法提高6.7%,表明其具有更好的精度与质量.
Study on Algorithm for Edge Detection of Mechanical Structures in Low Light Environment
This study made an attempt at proposing an object edge detection method based on multi-step image enhance-ment and adaptive morphological operations to address the issues of low accuracy and unsatisfactory results in mechanical structure edge detection in low light environments.This method utilizes the multi-scale Retinex algorithm to enhance the brightness and contrast of low light images,and combines sharpening and filtering algorithms to remove noise while pre-serving details.It uses adaptive morphological operations to remove pseudo edges and strengthen weak edges.In object edge detection experiment under low light conditions,the proposed method achieves a success rate of 86.7% which is 6 .7% higher than conventional methods,indicating its better accuracy and quality.

low light environmentmechanical manufacturingedge detectionadaptive morphology

介智登、张宏、谢霄

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江苏理工学院机械工程学院,江苏 常州 213001

江苏理工学院电气信息工程学院,江苏 常州 213001

暗光环境 机械制造 边缘检测 自适应形态学

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(16)