This is a serious lack of innovation opportunities in Machine Vision in Australia. The Australian Institute for Machine Learning – who are pushing to establish a National Centre of Excellence in Machine Learning – found that Australia is spending just a fraction of its GDP on artificial intelligence and machine learning when compared to its international rivals, and will lose jobs without increased investment in the technology.
While the US and China are leading the way in AI investment; Investment in AI and Machine Vision Australia is well behind comparable nations including South Korea, Singapore, and Japan. Australia was the first country to automate its ports and mine sites. Both industries are important parts of the nation’s history, and in many cases their culture! AI experts, engineers, and data scientists all agree that we have been presented with a huge opportunity to build on our innovative past and create a new foundation with Machine Vision and AI that will benefit the country and future generations.
There’s a call for Machine Vision in Australia
Now that it’s coming into its own, companies everywhere are exploring the benefits that Machine Vision brings. Especially in the automotive, machinery and agricultural engineering sectors. When it comes to innovation, machine vision is one of the most technically advance solutions for nations that seek to increase productivity and national income while developing new products and services capable of creating new business models, jobs, and opportunities. Overall production is growing at a steady rate. So the demand for machine vision will have influence over the coming years, supporting the market growth.
New opportunities for the use of Machine Vision will be everywhere. This is something Australia cannot escape! In reality – it’s already started: CSIRO’s Data61 has developed drones capable of travelling in GPS-denied environments utilising 3D LiDAR technology – they are able to travel down mine shafts to safely inspect hard to access areas of underground mines (so people don’t have to), while mapping along the way. The New-Zealand firm, Vector, has created a data-driven energy supply platform in Australia and New Zealand. Partners expect to enable tailored products for customers who are increasingly turning to solar power, batteries, and electric vehicles.
Overseas, a Harvard University group has spent 12 years creating a ‘robotic bee’. It’s capable of partially untethered flight thanks to artificial muscles beating the wings 120 times a second. The ultimate goal of this project was to create a swarm for use in natural disasters. The robotic bees provide artificial pollination. This is critical given the devastating effects of colony collapse disorder on bee populations and consequently food pollination.
Machine vision is going to be everywhere before long
There are many major advantages of MV. For example, increased productivity and high flexibility in the production process by improving the quality control inspection phase. An increase in accuracy and speed in automated material handling is another advantage.
This can directly benefit manufacturing firms and industries that are starting to understand the potential of MV systems. Especially when redundant or mundane tasks such as inspection, must have seamless precision.
So, how will you be sure that your business capitalises on this opportunity to be a first-mover, and maximise the benefits?
Machine vision isn’t just about the overall comprehension of the system and how those critical components interact with each other. It’s also about the fine details. The expertise of each component in the system interacting together to work reliably and generate repeatable results.
Machine vision is a set of technologies that gives machines greater awareness of their surroundings. It facilitates higher-order image recognition and decision-making based on that awareness. Machine vision makes sensors throughout the IoT even more powerful and useful. Instead of providing raw data, sensors deliver a level of interpretation and abstraction. This can be used in decision-making or further automation. Machine vision is usable with sensors, robots, and other IoT technologies. Just like another tech, machine vision can free up valuable employee time by performing repetitive, time-consuming tasks.
And that’s why, at Corematic, we’ve built a team of passionate engineers capable of collaborating with companies and providing an interdisciplinary approach to R&D and consulting.
We have a thorough awareness of entire vision systems. Imagine a robot that hits a button as soon as it is receives the prompt. It might sound simple enough. But, the entire process of making the robot move its arm at the right time, to the right place, with the right force, is far more complex than simply a ‘Visions System’.
Our expertise involves complex systems that enable environmental features to identify hardware using different types of information from RGB, infrared, and spectral cameras.
What does it mean?
It means that once this vision is available, the possibilities for use are endless. Human inspectors once had to touch and individually verify each workpiece as it came off the line. Now automated inspection stations have changed everything about the process. Now, we can utilise information that other systems cannot produce, and utilise robots for performing tasks that we cannot due to limitations such as size, distance, danger, or the mundane. We can remove certain human elements and the risk of inconsistencies, failures, or hazards that come along with them.
We are using Machine Vision to help businesses prevent parts damage and eliminate the maintenance time and costs associated. This benefits our customers in a wide range of industries from agriculture to biopharma; smelting to construction.
One of our latest technologies, TallyOp, has combined vision systems, sensing, and sensors to manage risks and increase productivity. This technology allows producers to not only identify defects during harvest but also generate a heat map to identify the best performing areas of a crop. This heat map becomes useful for soil testing in order to better replicate conditions. This allows for better-informed irrigation and fertiliser choices. Identifying and sorting defects can then occur at a far greater (and more accurate) rate than would be possible by hand. This technology gives farmers the information they need to better manage field health and have access to data in real-time for faster decision making and input. This results in improved product quality, higher yields, and lower production costs.
