5 Steps To Building Successful Computer Vision Systems

Computer vision is a branch of computer science that enables the computer to see, identify and process images in the similar way that human vision does and then deliver an appropriate output. Computer vision implants human intelligence and instincts to the computer. It is has a lot of resemblance to artificial intelligence as the computer must decide what it should see and then perform appropriate analysis.

But the goal of computer vision is not only to observe data but also to process and provide useful results based on observation.

The following are the rules for developing a custom computer vision application.

1. Understanding the Inspection Goals

There are several studies conducted which revealed that human minds are 80% only accurate in performing inspection application. These studies also demonstrate that automated computer vision systems prove to be more accurate, reliable, and tireless for executing these applications. The first task is to determine the cost budget and application benefits for the computer vision. The cost of the vision system must pay for itself over the short term by improving the manufacturing process and product yield or reducing the overhead, such as support cost, returns, and so forth. For example, in a practical computer vision application, a manufacturer realized that 15% to 20% of its product produced scrap because of process flaws and defects. The manufacturer estimated that if a computer-vision system could reduce its scrap by 50% then the resulting savings would exceed $1.7 million. Because of the huge potential savings, the manufacturer incorporated computer vision as a core manufacturing technology.

2. Estimation of Inspection Time

When product inspection time differs by a factor of ten from overall system inspection time then alternative technology should be considered. In some applications, the inspection requirements are such that the computer vision system cannot slow down the production process. When determining the processing time, look into the type of processing required. If the application involves gauging, then, a method called edge-detection is used. This technique processes only the pixels along a line profile for alignment and orientation of the part under inspection. If the inspection time cannot be optimized, then adapt to vision-software or systems integration.

3. Identify Features and Defects

Before incorporating a computer vision, there is a specific need for definition of the inspected part or what system cannot be automated. The computer vision demands answers to questions like what are the constituents. What are the common defects of the part? What are the visible features for a good or bad part? Statistical techniques can be used to help evaluate all the defects. If the defects cannot be classified, then the inspection process will generally be difficult to automate. Developing an image database of defects and acceptable components is important in developing a computer vision strategy. For this step, it is often useful to acquire images using an inexpensive camera, frame grabber, and lighting to evaluate whether the problem can be easily solved.

4. Selection of Lighting and Material Handling Methods

The objective of proper lighting and material handling is to highlight the part under test. Often, proper lighting and material handling devices reduce the software development time. Lighting and handling devices ensure that the image will be acquired inefficient manner which aids in simple software processing techniques. Lighting is critical for the successful development of vision systems for measuring, grading, sorting, monitoring, and controlling industrial inspection applications. In material handling, sophisticated machines such as conveyors, x-y positioning stages, robotic arms and motion control hardware and software are used. Overall, the manufacturing process, accuracy, and production speed reflect the accuracy of the vision system.

5. Selection of Image Acquisition Hardware

Selecting a camera and calculating the required image acquisition rate depends on whether the vision system will be positioned with a manufacturing line or used in off-site inspection purposes. The frame rate for monochrome cameras sets the minimum acquisition rate and processing time of the image-acquisition card. There are several ways of improving image-acquisition performance. Say, for example, some image-acquisition boards provide a programmable region of interest (ROI). A programmable ROI helps to optimize the data that are being transferred across the PCI bus for processing.