Manufacturing

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

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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.

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

Use cameras for automated object counting with deep learning.

Automated safety and compliance monitoring to minimize accidents and increase efficiency.

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Automatically detect unmasked people in public spaces or indoors.

Crowd detection application to identify human movement trajectories in public spaces.

Detect intrusion events in pre-defined areas by identifying the target's position, date and time.

Vision-based occupancy detection of public spaces and buildings.