Deep learning is a sub-field of machine learning that uses algorithms derived using the general function and structure of artificial neural networks. Deep learning simply attempts to copy the human brain, though it’s not at the same level of ability yet.
The neural networks used in deep learning allow a system to learn and detect patterns by processing large volumes of data. Many applications of AI rely on deep learning, since computers can be “trained” to perform critical tasks, without requiring any human oversight.
Deep learning is capable of recognizing patterns with a much higher accuracy than before. This opens up a world of new applications, especially for workplace safety. Deep learning is also being used in consumer applications, such as driverless vehicles.
In the workplace, deep learning can be used to recognize subject behavior, allowing companies to enforce better safety protocols and standards. For instance, deep learning algorithms can detect when a person isn’t wearing proper PPE, and alert them.
To work effectively, deep learning first requires copious amounts of labeled data. It processes millions of images and videos to be able to detect even the tiniest of differences between two images.
Deep learning can be used to make machines smarter, and reduces inefficiencies in the workplace. For instance, companies don’t need to hire several safety officers; they can just rely on deep learning to detect violations and alert employees.
Deep learning is also being used in other industries, such as electronics, aerospace, automotive, and medicine. From automated speech translation to safer driving on the road with automatic object detection, deep learning is changing the way humans interact with machines.
As mentioned above, deep learning algorithms rely on neural networks, which have several layers within. Generally, conventional neural networks contained only two or three layers.
Deep neural networks, on the other hand, can have as high as 150 or more. Using large datasets, technological firms can “train” deep learning models to identify specific features.
Convolutional neural networks (CNN) are a prime example of deep neural networks. They use 2D convolutional layers to process 2D data, making it an ideal choice for processing images.
More importantly, it doesn’t require any manual input, so the algorithm is capable of detecting different features directly from the images that are fed into the system.
This automated feature extraction makes deep learning a popular use with computer vision tasks, such as identifying or classifying objects. By carefully analyzing different layers of images, convolutional neural networks are able to accurately determine specific shapes and recognize them.
Think of deep learning as a more specialized form of machine learning. In deep learning, relevant features are detected and extracted automatically from an image. It relies on end-to-end training, where the network receives raw data and then learns how to process information on its own.
On the other hand, with machine learning, the features or distinguishing factors would have to be extracted manually from an image. Then, using the detected features, another model would be generated, which shall be used to categorize all the detected objects.
Arguably the biggest difference between the two is that deep learning algorithms start performing better as it processes more and more data. As the total size of data continues to increase, so does its accuracy.
The use of deep learning for workplace safety is becoming increasingly popular. Companies that have CCTV networks can use deep learning algorithms to detect when employees are violating safety protocols, such as not wearing proper PPE.
They can also be used to detect breaches, such as trespassing in an exclusion zone. Since deep learning algorithms are quite versatile, companies can also create custom safety rules depending on their safety standards.
This negates the need for companies to hire safety officers, and automates the entire process. It also improves workplace safety as employees can be notified when they are not using proper protection or abiding by safety standards.
Protex AI is a workplace safety solution that utilizes deep learning to help improve safety on a work site. It integrates seamlessly with existing CCTV networks, thus reducing the need for extra investment.
The plug and play nature of this solution makes it an ideal choice for use in all types of organizations. Automate unsafe event capture and put your EHS team in charge of making critical safety decisions using data-driven insights with Protex AI!