摘要
为提高开孔泡沫铝表面缺陷检测精度,提出基于傅里叶逆变换的检测法。制备开孔泡沫铝样品,采集样品表面图像,用自适应中值滤波算法预处理;用傅里叶逆变换分割缺陷区域,滤波后进行缺陷区定位,提取其特征,完成表面缺陷检测。结果表明:本方法可明显突出缺陷孔洞位置,无黑白点噪声干扰;识别表面缺陷时,误检率最高为1.4%,漏检率最高为2.8%,缺陷检测精度较高,具有一定的实用性。
In order to improve the accuracy of surface defect detection of aluminum foam with openings,a detection method based on inverse Fourier transform was proposed. A sample of open cell aluminum foam was prepared,the sample surface images were collected,pre-processed it with adaptive median filter algorithm. Inverse Fourier transform was used to segment the defect region. The defect area was located after filtering. The feature was extracted and the surface defect was detected. The results show that this method can obviously highlight the location of defective holes,and there is no black-and-white noise interference. When identifying surface defects,the highest false detection rate is 1.4%,the highest missed detection rate is 2.8%. The defect detection accuracy is high,and it has certain practicability.
作者
肖祯怀
XIAO Zhenhuai(Computer Department,Shangqiu Polytechnic,Shangqiu 476000,China)
出处
《兵器材料科学与工程》
CAS
CSCD
北大核心
2022年第1期133-136,共4页
Ordnance Material Science and Engineering
基金
河南省科技攻关项目(202102210158)
河南省高等学校重点科研项目(21B880029)。
关键词
开孔泡沫铝
零件表面缺陷
图像差分法
图像检测
傅里叶逆变换
open cell aluminum foam
surface defects of parts
image difference method
image detection
inverse Fourier transform
