多视图智能网络基于基因进化算法进行精确的3D测量
Yujing Qiao1, Ning Lv1, Baoming Jia2
1School of Mechanical Engineering, Yangzhou Polytechnic College, Yangzhou 225009, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了一种使用基因进化算法 (GEA) 进行低成本,多视觉3D测量的智能联网方法. 该方法提高了工业应用的精度和效率.
科学领域:
- 计算机视觉 计算机视觉
- 计量学 计量学 计量学
- 人工智能的人工智能
背景情况:
- 多视觉网络3D测量越来越重要.
- 整合低成本的工业摄像头以实现智能网络和高效测量仍然是一个挑战.
研究的目的:
- 为多视觉立体视觉3D测量提出智能联网方法.
- 解决应用低成本工业摄像机的局限性,以实现高效的3D测量.
主要方法:
- 深入分析多视觉立体视觉3D测量原理和网络约束.
- 基于改进的遗传进化算法 (GEA) 的智能网络方法的开发.
- 健身功能的动态校准和视觉球模型约束的整合,以获得最佳的观测距离.
主要成果:
- 提出的基于GEA的方法通过模拟和实验证明了其广泛的可行性.
- 达到的测量精度符合工业领域的严格要求.
- 成功建立了一个智能网络设计,用于集中多视觉系统.
结论:
- 开发的多视图智能网络算法为工业3D测量提供理论和技术支持.
- 为低成本和高效的现场3D测量解决方案提供了一条途径.
- 提高了多视觉网络3D测量技术的实际应用.
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