Scientific and technological advancements have significantly changed our industries and businesses. The rapid expansion of technology in industrial applications has produced new systems that expand the understanding of our surroundings.
Take computer vision technologies, for example. Equipping computers with the capability to extract profound insights from digital images and videos has advanced precision to a new level. This ability has enabled machines to gain a deeper understanding of information and data.
What Is Computer Vision?
Humans often draw inspiration from nature when creating new technology and innovating. Animal species generally use “eyes” to detect light patterns from the outside environment and interpret them into images.
With the advantage of time, humans have been interpreting images long before computers were invented. In fact, humans dedicate around one-third of the cerebral cortex to analyse and perceive visual information.
The process of isolating and understanding images, visuals, and videos has quickly developed beyond human capabilities. It has transformed the technology we use today by analysing and extracting meaningful data.
We can describe computer vision as using computers to process images from one or more cameras. This is done in order to extract specific information and numerical features. While cameras mimic the function of the eye, computers and data processors simulate the function of the brain. They analyse, understand, and infer data from the collected data in order to take action.
Using advanced computer vision models trained on specific tasks, machine learning and deep learning produce far more accurate and consistent results than humans can. This has enormous industry applications, such as object detection and classification, product inspection and defect recognition.
Computer vision use cases are numerous and varied. From facial recognition to highly advanced systems to identifying imperceptible defects in manufacturing, computer vision is changing how we work with technology.
Next-generation computer vision technologies can effortlessly understand and categorise abstract concepts and physical objects to find emotional, meaningful, and context-rich visual data. This can generate added value and positively impact manufacturing from the individual industry process up to the global supply chain.
Computer vision is critical to developing leading-edge AI and robotics technologies. It is opening the door to a wealth of business opportunities. For example, the autonomous inspection robot from Boston Dynamics, Spot Robot, that can ‘see’, navigate, and act on its surroundings based on visual data and advanced processing.
Computer vision is enabling technologists to push new boundaries across a vast expanse of industries. When implemented in industrial applications, computer vision can expand the boundaries of automation and self-driven machinery. It can add “intelligence” and expand system capabilities in fields such as automotive, consumer goods manufacturing, agriculture, defence, and retail.
How Can It Benefit Businesses?
The concept of computer vision has been around for decades. However, recent developments have pushed the boundaries of computer vision by combining it with emerging technologies like the Internet of Things (IoT) and Deep Learning (DL).
This fusion of vision and video analytics is enabling businesses to go beyond simple object identification. Visual data can now be classified, analysed, tracked, and transformed into actionable intelligence that can enhance business outcomes.
A key factor that makes computer vision the technology of choice is the ability to seamlessly integrate with existing video frameworks such as CCTV or machine vision systems. This, in combination with new-generation computer processors and cloud computing, gives businesses the ability to handle a large amount of complex data. It also generates insight evaluation through highly advanced models that can be run in real-time and as part of a production line or process.
In an increasingly competitive environment, businesses across industries are experiencing greater difficulty maintaining higher profit margins, thus making the utilisation of computer vision technology even more critical.
The use of advanced dashboards powered by computer vision data analytics will help managers track business operations in real time, predict machine downtimes, increase productivity and quality, minimise errors, and improve asset security and safety.
The future belongs to business leaders who can quickly adopt and integrate digital technologies such as Artificial Intelligence and Deep Learning into mainstream processes. Adopting computer vision technologies now will turn business operations into leaders of their industries in many ways, such as:
1. Continuous Process Optimisation
Seamless productivity has always been a high priority for manufacturers. Before computer vision technologies, companies relied on manual calculations and observations or complex production and supply chain analytics software to understand their operations and streamline processes.
Computer vision platforms provide the ability to see and interpret in near-real time by gathering data when required and analysing it on point to make time-critical determinations and agile business decisions.
For example, by combining real-time data with barcode reader technologies and active asset monitoring and search capabilities, factories can now speed up flow paths, optimise their layouts, and reduce downtime. This improves overall industrial efficiency, lowers supply chain costs, and builds automation and self-learning capacities.
Additionally, data-driven systems in production lines generate rich data and information that can be managed and analysed through generative deep learning models to predict machinery maintenance requirements and breakdown estimations, allowing for predictive manufacturing processes.
2. Improve Quality Control
Rigorous quality control processes are part of every successful manufacturer’s operations. But in highly regulated industries such as aviation, defence, and the pharmaceutical industry, a flaw in a product can risk not only reputation but could also pose a serious threat to human lives.
This is even more critical on high-speed production lines where high levels of the product are processed. In these cases, every component of the manufacturing activity requires quality detection and quality assurance to ensure a profitable outcome.
Computer vision far exceeds the role of humans, from the process of quality control and by measuring and inspecting with accuracy, repeatability, and speed. The rapid identification of issues could mark the line between saving resources or losing a whole production lot.
Utilising technologies that accelerate time-to-market while ensuring that quality is optimised improves efficiency, quality, and reliability.
3. Enhance Safety
Not only does computer vision reduce the need for human involvement in hazardous environments, it can also employ computer vision algorithms to safeguard employees near potentially harmful machines has proven to reduce workplace accidents dramatically.
Engineers can program computer vision technologies such as thermal cameras to detect overheating equipment, fires, and other safety hazards in industrial environments and notify machine operators to take action rapidly.
An advanced deep learning model based on visual data can be used to detect and analyse human behaviours, for area access control, or to identify unforeseen situations such as dangerous activity among employees.
Our Current Computer Vision Technologies
Technology and innovation should be accessible to all. That is why COREMATIC works hand in hand with businesses to identify its digital needs and automation opportunities.
Businesses can align computer vision technologies with advanced AI and data analytics to leverage the power of business intelligence. This can lead to an increase in competitive advantage. At COREMATIC, we use computer vision technologies in many of our projects and across many industries and applications.
Automotive
COREMATIC has developed several computer vision solutions for vehicle damage inspections in the automotive industry.
For one of our projects, we use multi-camera systems to scan vehicles. These generate prior damage reports for AI-based solutions that can detect, count, and track dents and defects in real-time.
COREMATIC has developed advanced models using computer vision that can detect hundreds of dents in real-time on vehicles with hail damage. The system then measures and classifies the severity of each dent. The system produces data that accurately estimates the repair cost in an automated manner.
Agriculture
Agtech is another field where technology is pushing the boundaries of computer vision and its applications. From soil preparation to food packaging, innovation can improve and make a difference to farmers and food producers.
COREMATIC developed a spraying system dedicated to the macadamia industry. It can effectively identify crop weeds and apply herbicide only when required. This system, based on deep learning models trained for specific object detection tasks, highly reduced the operation cost and among of chemicals applied to the orchard.
COREMATIC has also developed food quality control technologies to detect and classify sweet potatoes during the harvest process.
With the aid of artificial intelligence and advanced data collection, we created a yield monitor system capable of registering GPS tags every couple of meters to determine the amount and quality grade of potatoes directly after harvest.
The data collected through this technology enables farmers to improve their farm management decisions, resulting in increased efficiency and higher yields. This ultimately simplifies and enhances the overall process of harvest.
Biosecurity
COREMATIC has successfully utilised computer vision technologies and advanced mobile robots for biosecurity risk analysis applications.
Computer vision has improved safety and efficiency in undertaking visual inspections. Multiple camera technologies are combined to identify biosecurity risk material (BRM), enabling operators to make informed decisions.
By using these technologies, computer vision analysis was proven a feasible solution for automated critical asset inspections and monitoring, environmental data collection, and safety.
The Future Of Computer Vision
Computer vision is everywhere. Whether it’s barcodes being scanned at stores, smartphones unlocking with a slight glimpse of the user’s face, self-driving cars waiting at red lights or factories replacing repetitive jobs previously done by humans with robots. The most exciting thing is that computer vision technologies are only really just beginning.
It is estimated that the market for computer vision technology will reach $48 billion by the end of 2022. It will be the source of ongoing innovation and advancements.
Enabled by recent advances such as lightning-speed GPUs, computers with the ability to ‘see’ as humans do will completely change how we interact with them.
Previously, developing computer vision technology necessitated extensive servers and storage, resulting in high costs. However, it is now becoming more accessible to developers.
This is partly due to software abstraction, increased production, and reduced hardware costs. Increasing accessibility fosters innovation and the development of exciting new technologies. These advancements are currently in progress and will continue in the future.
Computer vision development will continue integrating with other technologies, such as AI, to create more agile and advanced applications. By merging image captioning apps, natural language generation, and advanced robotics technologies, we can improve human interaction with machinery. This approach will bring technology closer to humans.
Collaborative robots in production lines will use reinforcement and lifelong learning techniques. This will enable continuous improvement of production processes. Advanced cloud computing will power this, enabling the establishment of high-speed data pipelines. These pipelines will analyse vast amounts of visual data in real-time, generating statistics and insights.
As computers get faster, so too will the advancement of computer vision. We can now deliver better quality insights faster and at scale. To leverage computer vision systems, organisations should prioritize the desired outcomes. These outcomes may include enhancing safety, improving customer experience, boosting operational efficiencies, achieving sustainability and generating additional revenue.
