Issue link: https://resources.mouser.com/i/1437726
36 REIMAGINING WHAT'S NEXT Figure 1 shows the Object Size Detection pipeline. Let's explore this pipeline and the activities that occur. This image processing application uses images captured by a video camera mounted above a conveyor belt. A Convolutional Neural Network (CNN)—a type of image processing deep neural network—processes the captured images to determine if an object is present. First, the CNN identifies whether an object is in the capture frame. If an object is present, the CNN draws a bounding box and calculates the area that object occupies. Then, this area is checked against the predefined acceptable constraint. If the object is larger or smaller than expected, then a defect indication is communicated. Figure 2 shows the output of the Object Size Detection application of the OpenVINO ™ toolkit. Note that in this example, the CNN found the object and bounded it in order to calculate its area. The sample application also illustrates the use of the Message Queue Telemetry Transport (MQTT) protocol, which communicates the zone information to an industrial data analytics system. Why this is Cool Defect inspection is a monotonous task and prone to error based upon the inspector. Using deep learning to inspect parts frees up people to do more useful and creative work while increasing the efficiency of defect classification. In this simple example, the area of the part is used to determine if a defect exists, but deep learning can be applied in more advanced models to Figure 2: The Object Size Detector output screen shows an example of the calculated area of a detected object. Figure 1: The Object Size Detection Pipeline diagram illustrates how this application of the OpenVINO™ toolkit processes an image to determine whether an object has a defect based on its surface area.