Novel object motion estimation method for industrial vision systems in aligning machines

Abstract

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.