How AI inspection can reduce waste for manufacturers

Why Machine Vision and Deep learning go hand-in-hand for this scenario

Although machine vision systems tolerate some variation in a part’s appearance due to scaling, rotation, and pose distortion, complex surface textures and image quality issues introduce serious inspection challenges. Machine vision systems alone fail to assess the vast possibility of variation and deviation between very visually similar images.

traditional machine vision vs deep learning based image analysis

Source: Cognex

Deep learning-based systems are well-suited for visual inspections that are more complex in nature: patterns that vary in subtle but tolerable ways. Deep learning is good at addressing complex surface and cosmetic defects, like scratches and dents on parts that are turned, brushed, or shiny. Whether used to locate, read, inspect, or classify features of interest, deep learning-based image analysis differs from traditional machine vision in its ability to conceptualize and generalize a part’s appearance.

identifying defects in objects and characters from images

Challenging situations that can be effectively taken care of using Deep Learning (Source: Cognex)

And that’s not all

Here are more reasons to choose automated visual inspection when it comes to manufacturing:

Better Perception

Machine Vision has a very high optical resolution which depends upon the technology and equipment used for image acquisition.

Compared to human sight, machine vision has a ‘wider’ spectrum of visual perception with the ability to perform observations in the Ultraviolet, XRay and Infrared regions of the spectrum as well.

scope of machine vision with respect to visible spectrum

Faster — Observations as well as conclusions are made extremely fast, with the speed of a computer’s speed as measured in FLOPs and also, they result in precise calculations.

The system has all the power associated with higher processing speeds along with a potentially infinite memory capacity.

Reliable — The system is unbiased and programmable as required, following instructions without question.

Accurate — An automated system is capable of measuring absolute dimensions in a standardized manner.

Independent of Environment — Such a system can be deployed in dangerous and hazardous conditions or environments where human involvement may prove to be risky.

How to get started with Automated Visual Inspection

In terms of requirements, AVI does not really require much physical equipment. The equipment needed to start automating visual inspection can be split into hardware and software resources.

hardware and software in inspection

Hardware

These resources consist of primary equipments such as a camera, photometer, colorimeter and optional secondary equipment such as required for grading or sorting, which would be dependent on industry and automation processes.

We are essentially taking a picture and analysing the image, a camera is all you need!

Depending on the industry where it being use, the physical equipment can actually be categorized into three subsystems

Feeding system — Spreads items evenly and moves them at a constant speed, so that the optical system could capture frames of individual items.

Optical system — Consists of a specifically adjusted lighting source and a sensor (usually, a digital camera, technology glasses). The optical system captures images of inspected items so that the software can process and analyze them.