
The term computer vision is self-explanatory; computers having the power of sight enabling machines to see images and videos and to comprehend and identify content in these images and videos. Computer vision uses convolutional neural networks (CNN), the image equivalent of text transformers, CNN’s use algorithms designed to understand and analyse visual information by breaking it down into smaller parts called filters and then analysing them individually. Computers are trained on sets of images to identify certain objects and details, expanding the training data so that the computer can learn to find patterns on its own. Machines can identify objects, recognise faces, understand gestures, estimate depth, and calculate distances. This is the key technology used in autonomous vehicles. Self-driving cars are equipped with cameras and sensors that can detect obstacles, identify traffic signs, pedestrians, and other vehicles.
In Work Management computer vision can be leveraged by service providers for asset recognition and field resource assistance.
Image recognition offers a transformative potential for mobile field resources, enabling technicians to identify, diagnose, and address a wide range of issues related to asset maintenance and management directly in the field. This technology, integrated with mobile and handheld devices, provides a real-time, efficient, and highly accurate tool for preventative maintenance and failure diagnosis.
The application of computer vision is revolutionising the way issues and faults are recognised and diagnosed.
By harnessing AI-powered image recognition, technicians can use images captured by a mobile phone’s camera to identify signs of wear, damage, or malfunction in assets such as pumps, electrical feeders, or transformers. This system, trained on thousands of images of both faulty and non-faulty equipment, can differentiate normal from abnormal conditions, offering immediate feedback for early detection and diagnosis of failures.
Moreover, AI algorithms extend their capabilities to suggest potential root causes of identified issues by analysing visual patterns. This can pinpoint problems like corrosion, overheating, or physical damage, providing deeper insights into the underlying issues that need addressing.
The role of computer vision in preventative maintenance is further expanded through infrared and thermal imaging. Handheld devices equipped with infrared cameras capture thermal images, which AI then analyses to detect unusual heat areas. This functionality is invaluable for spotting overheating components in electrical systems or machinery, allowing for intervention before failures occur.
Integration with mobile work management systems ensures that AI-generated diagnostics are seamlessly uploaded via Bluetooth or Wi-Fi. This central storage of data enhances asset health tracking over time and the efficient scheduling of necessary repairs and maintenance. The system can autonomously generate work orders from AI diagnostics, prioritize them according to the severity, and assign them to the appropriate technician, optimizing field operations.
Additional applications of computer vision include safety inspections, where AI can identify hazards like exposed wires or unsafe conditions, prompting technicians to take preventive measures. It also plays a crucial role in quality control, ensuring that workmanship and materials meet predefined standards in construction or manufacturing. Furthermore, AI’s environmental monitoring capabilities detect conditions that could negatively affect assets, such as moisture levels and temperature fluctuations, enabling pre-emptive action to avoid failures.