Computer vision is one of several fields of artificial intelligence which allows computers to identify important information from imagery, videos, and other visual-based inputs.
In essence, computer vision uses artificial intelligence (AI) to learn about visuals and derive conclusions based on that information. It’s a way for computers to “see” things, much like human vision.
The primary difference between human vision and computer vision is that the latter needs a lot of training. The human brain is able to derive conclusions after seeing a particular scene almost instantly.
For instance, when you look at an object, you can categorise what it is, how far it is (depth perception), its state of motion, or spot any anomalies.
Computer vision isn’t as effective right out of the box. Since it leverages AI, computer vision uses deep learning models to improve its analysis.
Systems can be trained over time to process millions of inputs in a minute. This means, over time, computer vision becomes much more accurate and effective than humans, and can even identify issues that might seem imperceptible.
To work effectively, computer vision requires a lot of data. It analyses millions of datapoints repeatedly until it’s capable of identifying minor differences.
For instance, computer vision can be trained to identify humans. For it to happen accurately, the system is fed thousands of photos of humans, which allows it to eventually detect people in images.
Understanding how computer vision works is slightly complex. Currently, we are still not able to understand how brain computation works, we just have an idea about how neural nets operate.
And, since computer vision is effectively based on the same processes, it’s not exactly clear just how well computer vision works in comparison to the actual human mind.
However, broadly speaking, computer vision is all about recognising patterns. It uses advanced technologies like convolutional neural networks and deep learning to analyse millions of images that are fed into it.
Over time, the computer effectively teaches itself to distinguish between images. The system works on its own and doesn’t require programmers to oversee the learning processes.
Neural networks break machines down into tiny pixels. Each pixel is then tagged, and the network then analyses each pixel to predict the differences between them. As time passes, its predictions become more and more accurate.
Workplace safety is an important concern for organisations. Many companies use surveillance cameras on work sites and work with independent safety analysts to monitor employee activity and record any incidents.
In case something does go wrong, they use the recordings to identify what went wrong. This information can then be used by organisations to create more effective safety plans.
The use of AI technologies like computer vision can significantly improve workplace safety. As stated above, computer vision can be trained to discern between different images.
By using computer vision, you can easily accumulate data about regular processes and train a computer vision model to detect any safety lapses and issue a warning right away.
For starters, the use of PPE in certain workplaces is quite common. Computer vision can be trained to identify different pieces of PPE and send a notification to departments about using them. For instance, some of the many items it can detect include:
If you want to improve area control, you can specify exclusion zones, which are essentially unsafe areas that workers must avoid.
You can use specific models, such as light controls or crane area, or even define the maximum number of workers allowed in a particular space. This way, in case someone breaches the exclusion zone, a warning will be sent automatically.
Computer vision can be used to track behaviour too and identify any rule violations. For instance, it can detect when a worker breaks through a restricted height limit, or is close to coming in contact with electricity.
One of the best parts about using computer vision is that companies can define custom rules and train the computer vision to respond accordingly.
Depending on the industry you work in and the nature of risks that your employees face, computer vision can be trained to identify those and alert them in advance.
Protex AI is a dedicated workplace software module that integrates with your existing CCTV network. It can be used to identify any major risks and safety events, and take appropriate measures.
It can help companies improve safety in the workplace by allowing for more accurate reporting, and makes it easy for companies to understand different risks and prepare for them. It’s highly scalable and is a viable plug and play solution.