结合信息熵与低秩张量分析的金属零件破损检测

Damage Detection of Metal Parts by Combining Information Entropy and Low-Rank Tensor Analysis

针对当前金属零件破损处识别研究中自动化程度和识别精度较低等问题,提出一种结合信息熵与低秩张量的金属破损处检测算法。首先,运用差值法、中值滤波和傅里叶滤波等方法对图像进行去噪处理;其次,根据金属零件破损处与其邻域明显的差异性,采用信息熵边缘检测法获取边缘信息;最后,运用低秩张量分析差熵和权差熵矩阵以提取破损处,并与其他算法的结果进行对比分析。实验结果表明,本文算法能够有效并快速地识别金属破损处,检测结果噪声点较少,且该算法的有效精度高于80%,优于传统算法且具有较好的稳健性。

This study proposes an algorithm for detecting metal damage in combination with information entropy and low-rank tensor analysis to address the problems of low automation degree and recognition accuracy in the research of damage identification of metal parts.First,the image is denoised using the difference method,median filtering,and Fourier filtering.Second,according to the obvious difference between the damage of the metal part and its surroundings,the information entropy edge detection is used to obtain the edge information.At last,the low-rank tensor method is used to analyze the difference entropy and the weight entropy matrix to extract damage,and it is compared with other algorithms.The experimental results show that the algorithm can effectively and quickly identify metal damage with few noise points.The effective accuracy of the algorithm is higher than 80%with good robustness,which is higher than that of traditional algorithms.