Abstract：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.
韩硕,陈晓荣,张彩霞,郭蓉蓉,王晓龙. 基于改进模板匹配算法的物料计数方法研究[J]. 计量学报, 2022, 43(7): 863-868.
HAN Shuo,CHEN Xiao-rong,ZHANG Cai-xia,GUO Rong-rong,WANG Xiao-long. Research on Material Counting Method Based on Improved Template Matching Algorithm. Acta Metrologica Sinica, 2022, 43(7): 863-868.
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2022, Vol. 43 
