How Machine Vision and Deep Learning are changing the field of Automated Inspection in Manufacturing.

Artificial Intelligence is turning out to be a game changer, with countless applications in nearly every domain. It is now making its way into the area of Production and Manufacturing, allowing it to harness the power of deep learning and in doing so, providing automation that is faster, cheaper and more superior. This article aims to give a brief understanding of automated visual assessment and how a deep learning approach can save significant time and effort.

What is Visual Inspection ?

It involves the analysis of products on the production line for the purpose of quality control. Visual inspection can also be used for internal and external assessment of the various equipment in a production facility such as storage tanks, pressure vessels, piping, and other equipment.

It is a process that takes place at regular intervals of time, such as day-to-day. It has been shown repeatedly that visual inspection results in the discovery of most hidden defects during production.

When and where is Visual Inspection needed ?

While Visual inspection is used in manufacturing for quality or defect assessment, in non-production environments, it can be used to determine whether the features indicative of a “target” are present and prevent potential negative impacts.

Among the many industries where visual inspection is required, there are several where visual inspection is considered to be of very high consequence and is high priority activity due to the potentially high cost of any errors that may arise via inspection such as injury, fatality, loss of expensive equipment, scrapped items, rework, or a loss of customers. Such fields where visual inspection is prioritised include nuclear weapons, nuclear power, airport baggage screening, aircraft maintenance, food industry, medicine and pharmaceuticals.

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Why not just stick to manual inspection ?

While old might be gold, one could argue that there are several limitations to using the old fashioned way of inspection.

Manual inspection requires the presence of a person, an inspector who performs assessment of the entity under question and passes judgement on it according to some training or previous knowledge. No equipment is required except the naked eye of the trained inspector.

According to research, visual inspection errors typically range from 20% to 30% (Drury & Fox, 1975). Some imperfections can be attributed to human error, while others are due to limitations of space. Certain errors can be reduced through training and practice, but cannot be completely removed.

Visual inspection errors in manufacturing take one of two forms — missing an existing defect or incorrectly identifying a defect that does not exist (false positive). Misses tend to occur much more frequently than false alarms (See, 2012). Misses can lead to loss in quality, while false positives can cause unnecessary production costs and overall wastage.

Besides, manual inspection suffers from other limitations such as these

There remains the fact that the human eye, while more technologically advanced than any mechanical camera, can also be easily fooled. Consider some of these weaknesses associated with visual inspection:

Human Vision alone is undependable — Optical illusions such as those to the left can demonstrate just how unreliable the human eye can be. This does not necessarily mean that manual inspection is totally useless, but that it would be unwise to depend entirely on it.

Imprecision of eyesight— The human eye is incapable of making precise measurements, especially on a very tiny scale. Even while comparing two similar objects, the eye might not notice that one is slightly smaller or larger than the other. This concept also applies to characteristics such as surface roughness, size, and any other factor that needs to be measured.

Cost of labour – Manual inspection remains a costly venture due to the appointment of (multiple) trained individuals. Cost-wise, manual inspection operators may be earning a yearly salary of $50,000 to $60,000.

A new age alternative

Automated Visual inspection can overcome these problems by making the whole procedure of visual inspection independent of any human involvement. Using automated systems typically surpasses the standard of manual inspection.

Using deep learning and machine vision, it is not only possible but quite achievable to build smart systems that perform thorough quality checks down to the finest details. We don’t require walking talking android robots to automate production like inspection. Minimal physical equipment is needed to automate the visual inspection process. Instead the process is made smarter due to the use of Deep Learning. This approach typically involves steps such as image acquisition, preprocessing, feature extraction, classification etc. This is also classified in broad group image recognition.

What is Deep Learning and how does it factor in ?

Deep learning technology uses neural networks containing thousands of layers which are adept at mimicking human level intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns. In this way, deep learning merges the adaptability of human visual inspection with the speed and robustness of a computerised system.

Deep learning teaches machines to do what comes naturally to humans: to learn by example. New, low-cost hardware has made it practical to deploy a multi-layered “deep” neural networks that mimic neuron networks in the human brain. This gives manufacturing technology amazing new abilities to recognize images, distinguish trends, and make intelligent predictions and decisions. Starting from a core logic developed during initial training, deep neural networks can continuously refine their performance as they are presented with new images, speech, and text.

So what is Machine Vision then ?

Machine vision is the technology and methods used to provide image-based automatic inspection. It is a system that uses visual computing technology to mechanically “see” the activities that take place one by one along the production line. The components of an automatic inspection system usually include lighting, a camera or other image acquiring device, a processor, software, and output devices.

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Machine vision surpasses human vision at the quantitative and qualitative measurement of a structured scene because of its speed, accuracy, and repeatability. A machine vision system can easily assess object details too small to be seen by the human eye, and inspect them with greater reliability and lesser error. On a production line, machine vision systems can inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans.

A traditional automated system, while minimising costs and improving efficiency does not have the flexibility or tolerance for variation that human beings do. Manual inspectors are able to distinguish between subtle, cosmetic and functional flaws, and can interpret variations in part appearance that may affect perceived quality. Though limited in the rate at which they can process information, humans are uniquely able to conceptualise and generalize. Humans excel at learning by example and can differentiate what really matters when it comes to slight anomalies between parts. This begs the question of how machine vision can make the best choice, in many cases, for the qualitative interpretation of a complex, unstructured scene — especially those with subtle defects and unpredictable flaws.