An Improved FAsT_Match Algorithm for Micro Parts Detection

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI:10.1109/ICIVC50857.2020.9177440

Jia-yi Zhang, Y. Liu, Zhi-qiang Liu

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Abstract

Through analysis the characteristics of micro parts such as multi-categories, high detection frequency and similar shape in the classification process, based on the FAsT_Match algorithm, an improved algorithm of template matching recognition is proposed which is a Grid Region Optimized FAsT_Match (GRO FAsT_Match for short). Firstly, the method of gray level adjustment, global threshold image segmentation, boundary tracking and denoising is used to extract the smallest rectangle of the target part image as ROI area. Secondly, by calculating the scale relationship between ROI region and template image, the step sizes and limits of grid parameters for translation and scaling transformation are optimized. In order to improve the discrimination of normalized SAD distance for similar parts, uniform sampling of template image is adopted. The experimental data show that this algorithm features fast, precise, clear distinguish of similar parts, and meets the requirements of micro parts classification and detection. It has practical significance to improve the assembly efficiency of micro parts.

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微零件检测中一种改进的FAsT_Match算法

通过分析微零件分类过程中类别多、检测频率高、形状相似等特点，在FAsT_Match算法的基础上，提出了一种改进的模板匹配识别算法——网格区域优化FAsT_Match (GRO FAsT_Match)。首先，采用灰度调整、全局阈值图像分割、边界跟踪和去噪的方法提取目标部分图像的最小矩形作为感兴趣区域;其次，通过计算ROI区域与模板图像之间的尺度关系，优化平移和缩放变换网格参数的步长和限制;为了提高对相似部件归一化SAD距离的判别能力，对模板图像进行均匀采样。实验数据表明，该算法对相似零件的识别速度快、精度高、清晰，满足微细零件分类检测的要求。对提高微细零件的装配效率具有现实意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)

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