摘要
针对玉米种子机械裂纹检测准确率低的问题,提出一种基于双通道脉冲耦合神经网络(PCNN)模型的数字图像融合方法:1)运用离散小波变换(DWT)、非下采样轮廓波变换(NSCT)分别对预处理后的玉米种子机械裂纹图像进行分解,得到各自的高低频子带;2)对高低频子带系数分别采用不同链接强度的改进空间频率激励的双通道PCNN模型进行融合操作,得到融合后的高低频子带系数;3)通过NSCT反变换得到最终的玉米种子机械裂纹图像。试验结果表明:采用双通道PCNN模型检测玉米种子机械裂纹的准确率为97.2%;图像熵、相关熵、相关系数、均方根误差分别为0.3511、1.7314、0.9835和0.5263,整体优于LoG、DWT、NSCT和PCNN方法;双通道PCNN方法的单张图像的执行时间为14.9007 s,运行时间最长,但效果最优。
To solve the problem of low accuracy for mechanical crack detection in corn seeds,a digital image fusion method based on two-channel pulse coupled neural network(PCNN)model was proposed.Firstly,discrete wavelet transform(DWT)and non-subsampled contourlet transform(NSCT)were used to decompose the pretreated mechanical crack images of corn seeds,respectively,in order to obtain their high and low frequency sub-bands.Secondly,the high and low frequency sub-band coefficients are fused by using the improved spatial frequency excitation two-channel PCNN model with different link strengths.Thirdly,the final mechanical crack image of corn seed was obtained by NSCT inverse transformation.The experimental results show that the accuracy of the two-channel PCNN model is 97.2%,and the image entropy,correlation entropy,correlation coefficient and root mean square error are 0.3511,1.7314,0.9835 and 0.5263,respectively,which are better than the LoG,DWT,NSCT and PCNN methods.The execution time of single image by using dual-channel PCNN method is 14.9007 s,which has the longest running time and the best effect.
作者
张新伟
易克传
孙业荣
高连兴
ZHANG Xinwei;YI Kechuan;SUN Yerong;GAO Lianxing(College of Mechanical Engineering,Anhui Science and Technology University,Chuzhou,Anhui 233100,China;College of Engineering,Jilin Agriculture University,Changchun,Jilin 130118,China;Institute of Corn Breeding Engineering and Technology of Anhui Province,Chuzhou,Anhui 233100,China)
出处
《湖南农业大学学报（自然科学版）》
CAS
CSCD
北大核心
2022年第1期103-108,共6页
Journal of Hunan Agricultural University(Natural Sciences)
基金
安徽省教育厅项目(KJ2018A0542、KJ2018A0543、KJ2020A0068)
安徽省科学技术厅项目(202004a06020004、202104a06020001)。
关键词
玉米种子
机械裂纹
双通道脉冲耦合神经网络
检测
corn seed
mechanical crack
double-channel pluse coupled neural net(PCNN)
detection
