基于改进模板匹配算法的物料计数方法研究

针对生产过程中物料计数方法存在的缺陷,研究了一种改进的模板匹配算法进行物料数量检测。通过基于双模板搜索方式和位置约束系数r改进传统模板匹配算法(template matching algorithm,TMA),优化寻找最佳匹配图像的过程获得最优解,并以此进行图像拼接,完成物料计数。与传统模板匹配算法(TMA)和高斯金字塔优化的模板匹配算法(Gaussian pyramid transformation,GPT)进行实验对比,证明所提算法较其他2种算法在处理每一张样本图片的时间上分别由0.013s、0.011s缩短到0.008s,在计数准确度上分别由77.32%、84.94%提高到97.36%以上,并且在拼接精度上也优于其他2种算法,实验结果验证了所提算法的有效性。

Research on Material Counting Method Based on Improved Template Matching Algorithm

In order to solve the defects of the material counting method in the production process, an improved template matching algorithm is studied for material quantity detection, and the improved template matching algorithm (TMA) is optimized based on the dual template search method and the position constraint coefficient r. The process of finding the best matching image, obtaining the optimal solution, and then performing image stitching to complete the material count. Through experimental comparison with traditional TMA and Gaussian pyramid transformation (GPT) optimization, it proves that compared with the other two algorithms, the time to process each sample image is shortened from 0.013s, 0.011s to 0.008s, and the counting accuracy is improved from 77.32%, 84.94% to more than 97.36% respectively. The stitching accuracy is also better than the other two algorithms, and the experimental results verify the effectiveness of the algorithm.