计算机科学
人工智能
特征(语言学)
组分(热力学)
计算机视觉
机器视觉
计量系统
匹配(统计)
转化(遗传学)
深度学习
语言学
生物化学
热力学
基因
统计
物理
哲学
数学
化学
天文
作者
Zhihao Cheng,Yuan Sun,Kang Hu,Jie Li,Tien‐Fu Lu,Ruijun Li
出处
期刊:Measurement
[Elsevier]
日期:2023-11-01
卷期号:221: 113474-113474
标识
DOI:10.1016/j.measurement.2023.113474
摘要
Extensive manual intervention and management are typically required when using coordinate measuring machines (CMMs) for inspections in production lines leading to low efficiency. This study presents a deep learning–based intelligent measurement method and system for measuring typical features (including holes, cylinders, balls, steps, and slots) of common components to improve inspection efficiency. This method combines vision sensors and a trigger probe. The You Only Look Once algorithm was employed to learn and achieve intelligent detection of features. An image-matching algorithm based on image inverse perspective transformation was designed, and the ant colony algorithm was implemented to optimize the measurement sequence. Then, an automatic approach for feature measurement path planning was designed. The presented system was tested using CMM, and a component with multiple typical features was measured. Results show that this method and system can be efficaciously implemented for intelligent measurement of typical features.
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