Advancing with Machine Vision

You may be very familiar with the area of “Machine Learning”, but what about “Machine Vision”? What comes to your mind when you first heard about it? As illustrated by the words, this is the “eyes” of a machine that can visualise objects appearing in front of it. This technology comes with a system utilising digital input captured by a camera to determine the next action. Machine vision has been contributing significantly to industrial automation and manufacturing, mainly by performing an automated inspection as part of the quality control procedures. Indeed, it has been practised in real operation since the 1950s and began gaining traction within the industry between 1980 and 1990s.

Let’s first look into a simple example fill-level inspection system at a brewery to understand the technology better.

Inspection sensor detects the presence of beer bottle passing through it, which triggers the vision system to lighten that specific area and capture an image of the bottle. Frame grabber (a digitising service) translates the image taken into digital output. The next step is followed by storing the digital file in memory to be analysed by the system software. In the end, direct comparison is made between the file and predetermined criteria to identify defects. If an incorrectly filled bottle is detected, a failed response is delivered signalling a diverter to reject the bottle. The operator can also view discarded bottle and real-time data on display.

Figure 1: Example of bottle fill-level inspection

This example demonstrates the usefulness of machine vision in automating daily inspection task carried out by workers, further boosting daily productivity, and bringing significance difference onto operational profit. However, such a system could only be realised by the combination of software and hardware, and the type of equipment needed in each vision system would be subjected to different requirements. Those typical components installed includes: 

  • Sensors
  • Frame-grabber
  • Cameras
  • Lighting 
  • Computer and software 
  • Output screen or relevant mechanical components

Besides, there are currently three categories of measurement for the machine vision system:

 1D, 2D and 3D.

  • 1D Vision System: Instead of looking at a whole image at once, 1D Vision analyses a digital signal one line at a time. This technique usually detects defection in materials manufactured in a continuous process, such as paper, cardboard and plastics.
  • 2D Vision System: Mostly involved inspections that require a range of measurements such as area, perimeter, shape, resolution, entre of gravity etc.
  • 3D Vision System: Made up of multiple cameras or one or more laser displacement sensors. The latter allows the measurement of volume, shape, surface quality and also 3D shape matching.

Uses and Advantages

Throughout the years, machine vision has been integrated with technologies such as machine learning and deep learning to better harness the usefulness of data to improve a machine’s autonomous behaviour on encountering variations. The figure below shows vivid examples of enhancing machine vision with artificial intelligence in the manufacturing and construction industry.

Figure 2: Examples of integration between machine vision and artificial intelligence

These examples show how artificial intelligence can lift the use of machine vision onto another level. Up until today, machine vision remains to have the highest coverage in industrial application due to the ease of use and multiple direct advantages offered to manufacturers, with the main ones listed as below. 

  • Enhance product quality: Manufacturer can replace sample testing with 100% quality checks done via a camera system. Every batch of products can be reliably checked for flaws during the production process without any interruption.
  • Cut production cost: Through detailed visual inspection, defective parts are removed from the production process since the start. These faulty products do not proceed to the upcoming manufacturing stages and contribute to costs. Also, materials cost is saved by re-introducing them back to the production process at later stages. The system may also ‘self-learn’ to recognise recurrent defects. Such statistical information and behaviour would be absorbed into the system to understand the source of problems, further improving the system’s performance. 
  • Improve the efficiency of production: Many products are still assembled manually, and machine vision integrated system can replace human labour. Workers could be allocated to other stages of production that require more workforce and human supervision. Moreover, machine vision works under excellent precision and speed for a long time, overcoming human’s disadvantage of feeling fatigue.
  • Error proofing: Human eye has its limitation in inspecting complex applications. The assistance on machine vision significantly brings downs the risk of misassembled products. Its system equipped with the right imaging specifications and software can quickly identify details that are hindered by the human eye.

Moving forward with machine vision

In the coming years, the global machine vision market is predicted to grow from USD 9.6 billion in 2020 to USD 13.0 billion by 2025, at a CAGR of 6.1% during the forecast period. This forecast is attributed to the growing demand for vision-guided robotic system and increasing application in pharmaceutical and food packaging industry in the wake of COVID-19. The COVID-19 pandemic has led manufacturers to realise the importance of automation in manufacturing that largely reduces human intervention involved in the process.

It is also notable that APAC countries such as China, South Korea and Japan are expected to hold major market share as they own some of the most extensive manufacturing facilities and autonomous manufacturing. It shall be a call for Malaysia to grab ample growth opportunities within the region by increasing the use of machine vision in the manufacturing industry. The fact that manufacturing has been contributing to the 2nd largest share in Malaysia’s GDP over the years shall build us a strong foundation to further apply this technology in scaling up the sector. Higher usage of machine vision would then drive down the capital cost required to acquire these software and hardware equipment tagged with considerable prices in the market. Regional factors, the unpredicted resurgence of COVID-19 and long-term benefits should urge Malaysian manufacturers to elevate its adoption level in machine vision before losing its competitive edge globally in producing electrical, electronic, rubber, chemical products etc. 

Despite the positive outlook on market forecast, multiple challenges lie ahead on better and smarter usage of machine vision technology to unleash its vast potential. Development of technology has to keep in pace with human’s increasing demand over time, or even one step ahead. First, there are still uncertainties about the application of deep learning in machine vision, which uses convolutional neural networks (CNNs) to perform classification tasks, by learning from a set of training images in improving its identifying characteristics. Although processor and tool resources are considered sufficient, the number of available training images are still limited.

Next, the adoption level of machine vision in the non-industrial application remains at the infancy stage. Areas like driverless cars, autonomous farming, guided surgery and other non-industrial uses require more significant input of development and validation in ensuring its practicability to the market. These could be a vital part for the future growth of machine vision, instead of placing sole focus on the manufacturing industry that rides on the right track. Other than that, there are also challenges when it comes to the integration of 3D imaging for specific applications. Not all of the 3D machine vision applications are “ready for prime time”. For example, most 3D systems are capable of picking homogeneous (all the same) objects but picking heterogeneous and unknown objects possesses a challenge for 3D imaging.

Moreover, performing 3D imaging to reconstruct surface or object for measurement and differentiation purposes can be quite challenging at the scale of production. This is because a high volume of images is required to completely model and analyse the part. It is noted that there are also challenges in other areas such as embedded vision and robotics that were not laid out in this article to prevent over-enlarging the scope of discussion.

In conclusion, machine vision technology is leading its way into applications inside and outside of factory settings, gearing towards the path of Industry 4.0. It is a capability instead of an industry that can be integrated into various processes and technologies for greater convenience and business efficiency. We can expect to witness greater innovation and breakthrough in machine vision through the evolvement of artificial intelligence demonstrated over the years. In addition, the low possibility of social distancing measurements ending in near-term gives rise to a unique opportunity for machine vision in meeting business demands with reduced labour.

Written by Lim Khey Jian, Intern at 27 Advisory. Currently pursuing his degree in Chemical Engineering at The University of Manchester. He takes problems and difficulties as opportunities to grow. He enjoys badminton, football and books related to governance, economics and personal development and aims to contribute to society in any way possible. He believes that Malaysia has a lot of potentials to grow as a country and he is always ready to play his part as the nation moves forward. 

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