Industrial vision systems have been adopted in many manufacturing processes for monitoring manufacturing procedures. With the recent proposed concept of industrial information integration engineering (IIIE) (Da Xu, 2020 [1]), the adoption of industrial vision systems involves a wider IIIE context as a data acquisition measurement, and thus higher level intelligence like control automation is expected. Industrial vision systems have been used for object alignment in aligning machines. By coping with a pneumatic device, misaligned objects can be detected by vision systems and rejected. Since alignment efficiency depends on the appropriateness of the blown timing/pressure, which is casually manifested in the motion of the observed blown objects, a motion estimation method for industrial vision systems must be adopted. Although conventional motion estimation methods in this field typically use pre-prepared templates, this paper proposed a novel object motion estimation method based on the properties of industrial images. A bounding box within a confined observation area is first initialized for each object, and then motion estimation is achieved by updating the bounding box following the expectation–maximization principle. Experiments revealed that our proposed method achieved robust and continuous motion estimation of the parts and reduced the processing time compared to conventional template matching methods.

工业视觉系统已在许多制造过程中用于监控制造过程。随着最近提出的工业信息集成工程（IIIE）的概念（Da Xu，2020 [1]），工业视觉系统的采用涉及更广泛的 IIIE 上下文作为数据采集测量，因此期望控制自动化等更高级别的智能. 工业视觉系统已用于对准机器中的对象对准。通过使用气动装置，视觉系统可以检测到未对准的物体并拒绝。由于对准效率取决于吹气时间/压力的适当性，这在观察到的吹气物体的运动中偶然表现出来，因此必须采用工业视觉系统的运动估计方法。尽管该领域的传统运动估计方法通常使用预先准备好的模板，但本文提出了一种基于工业图像特性的新的物体运动估计方法。首先为每个对象初始化一个受限观察区域内的边界框，然后通过遵循期望最大化原则更新边界框来实现运动估计。实验表明，与传统的模板匹配方法相比，我们提出的方法实现了对零件的稳健和连续运动估计，并减少了处理时间。首先为每个对象初始化一个受限观察区域内的边界框，然后通过遵循期望最大化原则更新边界框来实现运动估计。实验表明，与传统的模板匹配方法相比，我们提出的方法实现了对零件的稳健和连续运动估计，并减少了处理时间。首先为每个对象初始化一个受限观察区域内的边界框，然后通过遵循期望最大化原则更新边界框来实现运动估计。实验表明，与传统的模板匹配方法相比，我们提出的方法实现了对零件的稳健和连续运动估计，并减少了处理时间。