SUMMARY
SUMMARY
Take a minute to write an introduction that is short, sweet, and to the point.
Introduction
Object recognition in industrial or processing environments has wide implications for improving different control systems. Object detection is a technique that can identify multiple prominent objects within a captured image, as well as the object’s bounding container. Given a processing plant that theoretically sorts high volumes of non-correlated objects into different physical containers, then image sensing and machine-learning based detection is an appropriate method to help automate this organisational system. The given plant will organise apples, mugs and shoes into different bins. These objects are vastly different which justifies the use of machine-learning techniques over more traditional image processing and segmentation methodologies.
This project examines the controller component of the scope where an image is captured, processed and appropriate results are fed back to an operator or as a control signal to a process or actuator to physically move the objects into appropriate bins. The given processing plant can be mottled as an open-loop feedback system where an object is placed on a conveyor belt, recognised and detected, before being led through an appropriate gate to a storage bin.
Methodology
The methodology section of this project examines the steps required for implementing the object detection model into a working controller application.
import SwiftUI //-- Import SwiftUI visual element library
@main //-- Application entru point
struct RoboticsVisionAssignmentSUIApp: App { //-- Create a struct that conforms to the App protocol
//-- Create the body scene as a requirment of the App protocol
var body: some Scene {
WindowGroup { //-- Create a window group
//-- Render the image view which also handels image processing
DisplayImagesView()
.padding(20) //-- add some padding and set the miminum and ideal frame heights
.frame(minWidth: 1600, idealWidth: 1600, minHeight: 900, idealHeight: 900) //
}
}
}