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基于机器视觉技术的新能源汽车零部件表面缺陷检测

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新能源汽车零部件表面缺陷种类繁多,依靠人工检测存在错检、漏检等问题。为了提高新能源汽车零部件表面缺陷精度,提出基于机器视觉技术的新能源汽车零部件表面缺陷检测方法。结合Ridegelet变换与小波变换,在不破坏图像细节前提下,对基于机器视觉技术采集的新能源汽车零部件表面缺陷图像展开去噪处理。引入MSR算法,通过信息熵占比权值的自适应计算,实现图像的快速增强。利用数学形态学计算方法,对新能源汽车零部件图像中的缺陷主体展开边缘提取,进而实现新能源汽车零部件表面缺陷的检测。实验结果表明,所提方法检测耗时低于10 ms、缺陷位置分析准确,面对多类型新能源汽车零部件表面缺陷,能够做到精准识别,提高了新能源汽车零部件的缺陷检测效率。
Surface defect detection of new energy vehicle components based on machine vision technology
There are various types of surface defects in new energy vehicle components,and relying on manual in-spection has problems such as false or missed inspections.In order to improve the accuracy of surface defects in new energy vehicle components,a surface defect detection method for new energy vehicle components based on machine vi-sion technology is proposed.Combining Ridegelet transform and wavelet transform,denoising is performed on surface defect images of new energy vehicle components collected based on machine vision technology without damaging image details.MSR algorithm is introduced to realize fast image enhancement through adaptive calculation of information en-tropy proportion weight.The mathematical morphology calculation method is used to extract the edge of the main body of defects in the image of new energy vehicle parts,and then the surface defects of new energy vehicle parts are detec-ted.The experimental results show that the proposed method has a detection time of less than 10 ms and accurate de-fect location analysis.In the face of surface defects in multiple types of new energy vehicle components,it can achieve accurate identification and improve the defect detection efficiency of new energy vehicle components.

Ridegelet transformationwavelet transformmachine vision technologyMSR algorithmcircularity operator

黄爱维、钱辉、牛华

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南通理工学院,江苏南通 226000

Ridegelet变换 小波变换 机器视觉技术 MSR算法 circularity算子

教育部产学合作协同育人项目中青年骨干教师培养专项江苏省高等学校普通高等学校自然科学研究项目

201802244006ZQNGGJS20220622KJD580004

2024

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子:0.74
ISSN:0253-2743
年,卷(期):2024.45(6)
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