Zhang Hui, Guan Zhen, Eastwood Joe, Zhang Hongji, Zhu Xiaoyang
School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Manufacturing Metrology Team, Faculty of Engineering, University of Nottingham, Nottingham NG8 1BB, UK.
Sensors (Basel). 2023 Aug 21;23(16):7300. doi: 10.3390/s23167300.
The accurate identification of highly similar sheet metal parts remains a challenging issue in sheet metal production. To solve this problem, this paper proposes an effective mean square differences (EMSD) algorithm that can effectively distinguish highly similar parts with high accuracy. First, multi-level downsampling and rotation searching are adopted to construct an image pyramid. Then, non-maximum suppression is utilised to determine the optimal rotation for each layer. In the matching, by re-evaluating the contribution of the difference between the corresponding pixels, the matching weight is determined according to the correlation between the grey value information of the matching pixels, and then the effective matching coefficient is determined. Finally, the proposed effective matching coefficient is adopted to obtain the final matching result. The results illustrate that this algorithm exhibits a strong discriminative ability for highly similar parts, with an accuracy of 97.1%, which is 11.5% higher than that of the traditional methods. It has excellent potential for application and can significantly improve sheet metal production efficiency.
在钣金生产中，准确识别高度相似的钣金零件仍然是一个具有挑战性的问题。为了解决这个问题，本文提出了一种有效的均方差（EMSD）算法，该算法能够高精度地有效区分高度相似的零件。首先，采用多级下采样和旋转搜索来构建图像金字塔。然后，利用非极大值抑制来确定每一层的最佳旋转。在匹配过程中，通过重新评估对应像素之间差异的贡献，根据匹配像素的灰度值信息之间的相关性确定匹配权重，进而确定有效匹配系数。最后，采用所提出的有效匹配系数来获得最终的匹配结果。结果表明，该算法对高度相似的零件具有很强的判别能力，准确率为97.1%，比传统方法高11.5%。它具有出色的应用潜力，能够显著提高钣金生产效率。
