粉状炸药自动包装工艺的改进研究

目的 针对印刷品表面缺陷检测中计算实时性差、缺陷类型识别率不高等问题，提出一种改进灰度共生矩阵(GLCM)的印刷品表面缺陷检测方法。方法 首先对主流的缺陷检测流程进行优化设计，通过对图像进行预处理和差分操作，判断待测印刷品表面是否存在形状缺陷;然后针对传统灰度共生矩阵的特征提取维度高、信息易丢失、旋转不变性差等问题，设计一种综合考虑效率和实时性的缺陷区域特征参数提取算法;最后结合得到的特征参量，通过基于支持向量机的分类器完成不同形状缺陷的分类识别。结果 实验结果表明，文中所设计的改进算法所提取的特征参量更能精确表征缺陷区域的特征，同时，特征参数的提取时间和缺陷分类识别率等指标均比传统检测方法更有优势。结论 在保证计算实时性的前提下，文中所设计的检测方法能有效完成印刷品表面缺陷区域的纹理特征识别能力，具有较高的分类识别率。

Improvement of Automatic Packing Technology for Powdered Explosives

The work aims to propose a new method of surface defect detection based on improved gray level co-occurrence matrix to solve the problem of poor real-time calculation and low defect type recognition rate in the detection of printing defects. Firstly, the mainstream defect detection process was optimized to determine the presence of shape defects on the printing surface to be tested by pre-processing and differential operation of the image. Then, a feature parameter extraction algorithm for the defective region was designed to address the problems of high dimensionality, easy loss of information and poor rotational invariance of the traditional gray level co-occurrence matrix. Finally, combined with the obtained feature parameters, the defects were classified and recognized by the classifier based on support vector machine. The experimental results showed that the feature parameters extracted by the improved algorithm designed in this paper can more accurately characterize the features of defect areas. Meanwhile, the extraction time of feature parameters and the defect classification and recognition rate were more advantageous than the traditional detection methods. On the premise of ensuring real-time computation, the detection method designed in this paper can effectively identify the texture features of the defect areas on the surface of printing and has a high rate of classification and recognition.