The Use of Computer Vision Methodologies to Estimate the Volume of Powdered Substance Shapes

Many compressed air devices are energy inefficient. One example is using air nozzles above pastry lines to remove flour and cool products. These nozzles consume excessive energy, particularly when mounted too high, requiring stronger airflow. Adjustable nozzle height and energy-efficient nozzles should be used with careful control of air pressure, flow rate, and activation time, ensuring efficient and adaptive control. Additionally, sensor-based control should activate airflow only when pastries are present and until the correct amount of powder material has been blown out, as the nozzles often operate unnecessarily. Accurate measurement of powder volume after blow-off remains a challenge. With the use of computer vision methodology, the system would continuously read the measured values and determine not only the optimal moment to interrupt device operation but also dynamically adjust key parameters. This paper demonstrates that computer vision can estimate powder volume using two non-contact 3D methods: a depth camera, and a structured light scanner. Their accuracy, reliability, advantages, and limitations are analyzed. The results show that the structured light scanner can be used in the case of a static model (the conveyor belt with products stops at the moment when it is necessary to perform a 3D measurement). This approach shows higher repeatability and gives a more accurate 3D model. On the other hand, for the dynamic model (the conveyor belt with products moves while the 3D measurement device is fixed), the depth camera can be used because, at minimum rotation speeds of the substrate, it shows higher accuracy and enables faster adaptive modeling and creation of the necessary data.