Applications

Using computer vision, various industries can predict and detect heavy machinery breakdowns by monitoring early signs of wear and tear, and avoid costly breakdowns. These Computer Vision systems can continuously monitor machinery and efficiently recognize gradual wear on machinery components that might not be visible to the human eye. The Need for Computer Vision In traditional methods dedicated teams are tasked with monitoring heavy machinery, which is time time-consuming process, and cannot be done continuously, which results in missing signs of breakdown, that could have been prevented. Computer vision solves this problem, it not only runs continuously but also is highly accurate at detecting signs that if often invisible to the human eye. Additionally, the machines don't need to be stopped for inspection, but they can keep running while the system monitors machinery. This reduces downtime and also improves the life span of heavy machinery. These systems are highly accurate at detecting cracks or deformation, for example, a conveyor belt with minor surface abrasions detected early through visual analysis can prevent larger tears or shutdowns. Additionally, these systems also monitor machinery movement and flag any kind of unusual behavior that might indicate malfunctioning. These systems capture visual data to assess

Computer vision systems detect and monitor wall and foundation cracks for early intervention and improving structural safety.

Computer vision systems on worksites can enhance safety by monitoring behavior around restricted areas and hazardous zones in real time.

Computer vision ups the quality of store security and customer experience by automating self checkout processes and closing efficiency gaps.

Learn how computer vision systems can be applied in the real world for automating traditionally manual and time-consuming defect detection.

Computer Vision application to read analog dials such as gauges, digital displays, and warning light colors using cameras.

Product detection uses deep learning to detect different product types automatically.

Automated detection of potholes with AI vision

Automated Number Plate Recognition to identify vehicles in real-time.

AI-based ergonomic risk analysis using cameras to detect the human posture of employees.

Use cameras for automated object counting with deep learning.

Eye tracking and gaze detection for monitoring the attentiveness and stress levels.