高聚物黏结炸药CT图像像素级裂纹识别方法研究

为了有效地提高裂纹识别精准度和改进裂纹表征方法,针对奥克托今(HMX)基高聚物黏结炸药(PBX)CT检测图像存在的暗视野、低对比度、高噪声等问题开展研究。利用裂纹的稀密性和局部方向性,结合非局部均值、拉普拉斯锐化和Gamma校正等方法对CT图像进行预处理,然后运用统计学方法直接提取裂纹特征点集,借助马氏距离对裂纹特征点的局部方向性和裂纹位置、走向及粗细进行判定并推演裂纹的扩展方向,最终通过像素灰度值拷贝实现HMX基PBX的CT图像中完整裂纹的精细识别。在三种PBX热力耦合加载损伤的CT图像上对文中提出的MDCE算法(Mahalanobis distance based crack extraction algorithm)与Canny算法和相位一致性方法开展对比实验研究。结果表明,提出的MDCE算法能清晰准确地提取出多种形态的裂纹,证实了该方法的有效性和高效性,显著提高了裂纹的识别和表征能力。

Research on Pixel-level Crack Extraction Method for CT images of Polymer Bonded Explosives

In order to effectively improve the accuracy of crack identification and crack characterization method,this research has focused on the problem of dark fields,low contrast and high noise in the computed tomography(CT)images of HMX-based polymer bonded explosives(PBXs).We propose a new algorithm.By using the density and local-orientation of cracks,the algorithm first preprocesses the source image by integrating the non-local mean algorithm,the Laplacian sharpening algorithm and the Gamma sharpening algorithm.Then,it adopts the statistical method to extract directly cracks’feature-points,which is used to derive the positions,orientations and thicknesses of cracks via the local orientation among these points measured by the Mahalanobis distance.Finally,it copies the gray values of pixels from the source image,to realize fine recognitions of continuous cracks from CT images of HMX-based PBXs.Comparative experiments were conducted,on CT images of thermal-force compound loading cracks from three PBXs,by this proposed Mahalanobis distance based crack extraction(MDCE)algorithm,Canny edge detection algorithm and phase consistency method,respectively.Results show that this proposed MDCE algorithm can extract various cracks clearly and accurately cracks from CT images with low quality,which indicates the high effectiveness of this method and enhances the ability of crack recognition and characterization.