Video analytics for modern manufacturing

Modern manufacturing is becoming more complex by the day. Sites are dynamic and high-risk, and the increasingly competitive landscape requires constant innovation across all areas, which is seeing a rise in the adoption of automation, IoT and AI technologies.

Findings reported by the UK Research Institute reveal that manufacturers that prioritise technological innovation and development grow twice as fast as those that don’t. This highlights how much of a supportive act digital technology can be in this charge, due in part to the increased productivity levels that follow from implementing the right technologies.

One of the key challenges in manufacturing today is the need to balance quality and efficiency with cost reduction, whilst remaining compliant with continuously changing guidelines, protocols and regulations.

Manual processes remain a significant bottleneck, impacting key areas such as quality control, inventory management, health and safety and predictive maintenance. As such, cutting-edge technology and automation that help alleviate these issues are now essential to meet the requirements of a modern factory.

However, in addition to optimising the production line, manufacturers are also increasingly turning to digital technologies to optimise warehousing, staff resourcing and the health, safety, and security of their sites too. These areas are not only vital for minimising loss but for avoiding delays to production which can cause significant disruption.

The power of computer vision in manufacturing

Increasingly, forward-thinking manufacturers are looking to the capabilities that AI can offer in their quest to maximise efficiencies and enhance site safety and security. According to Deloitte, adopting computer vision automation, and other smart factory initiatives accelerates manufacturing cycles, resulting in a 12% growth in labour productivity and 10% in total production output.

Computer vision uses visual inputs (such as images and videos) to obtain and extract meaningful data captured by devices deployed in strategic locations around a facility.

The information recorded is analysed by algorithms that can then automate various tasks and help influence decision making, from detecting almost invisible damages to important machinery to identifying high-risk floor areas and near-miss safety incidents.

Here are some of the main applications for how computer vision can help provide significant benefits to manufacturers:

Quality control

Ensuring consistent product quality can be difficult with manual inspection, which can be both time and resource intensive. It’s also susceptible to human error, which can occur no matter how experienced the inspector may be.

Already, many modern manufacturers are utilising computer vision to automate important visual quality checks during the production process. The technology can identify product defects with higher accuracy, repeatability and speed than any manual process, preventing costly oversights or errors as well as improving efficiency.

Inventory Management

Manually tracking inventory is time-consuming and prone to errors. Computer vision technology can streamline this process, by monitoring parts and inventory in real-time to recognise, locate and measure items and their movement through warehouses or distribution centres. This eliminates the need for manual counting and possible data entry errors as well as providing insights into the flow of physical processes, helping to make decisions to optimise spaces and workflows.

Health and Safety

Enforcing safety protocols and identifying near misses can be challenging. Understanding where staff are at different times and monitoring high-risk areas is a complex task to balance alongside other site responsibilities. As a result, many areas of manufacturing sites are not routinely monitored.

There are certain dangers associated with manufacturing line machinery, including slips and falls, the presence of smoke, and the presence of objects or people in danger zones, such as those for heavy machinery or vehicles. All of these, combined with the struggle to monitor all areas of a facility effectively, increase the likelihood of safety and security incidents.

Here, computer vision can be used to detect safety violations, such as improper PPE usage or personnel entering restricted areas. It can also identify ‘near misses’ to proactively improve safety protocols and be used to monitor and enforce safety protocols in loading/unloading areas, car parks, and other areas where heavy machinery is in constant operation. Computer Vision can also act dynamically to alarm when people are entering into proximity with machinery that is explicitly running or in motion.

Predictive maintenance

Manufacturers lack the real-time data needed to predict equipment failures, and similar to the issues surrounding manual quality control, the human eye is fallible to missing small defects or faults with important machinery. If detected or unaddressed, the implications of this could be catastrophic - not just to production lines in terms of output, but from a health and safety perspective too.

Malfunctioning equipment poses a huge risk, which predictive maintenance can prevent, along with helping to avoid any unnecessary downtime caused by out-of-action equipment. By analysing video footage of equipment, computer vision technology can predict potential failures and schedule maintenance before disruptions occur.

These are just some of the main applications for computer vision in manufacturing being used today. In the future, we’ll certainly see wider AI technologies adopted to further ends, from creating digital twins of factories and enabling virtual simulations for process optimisation, to enhancing robot capabilities to enable safer and more efficient human-robot collaboration.