What Is Object Counting With Computer Vision?
Object counting with AI vision methods is a popular computer vision application used to detect and count objects in a scene. Therefore, machine learning models are trained to recognize specific objects in videos images.
Industrial vision systems to count objects using cameras are popular in manufacturing, to recognize and count products, pieces, and boxes produced. The traditional machine vision methods are increasingly replaced by deep learning methods that are significantly more flexible and easier to apply.
Features of Object Counting
Deep neural networks are trained to recognize specific objects in real-time by processing the images of a video feed.
- Pre-trained or custom-trained AI models for computer vision to detect specific objects (classes).
- Automated object Detection and localization of the detected objects.
- Optionally, object classification can be applied to determine the item type (for different variants).
- Conditional logic and business workflows as required by the specific use case.
- Focus the counting on specific areas within the camera stream (e.g. in a room, on a table, or on a conveyor belt).
- Privacy-preserving edge AI with real-time image processing on-device (no need to send videos to the cloud).
Value of Real-Time Object Counting
Using AI and cameras to count objects provides a highly scalable and effective approach to automate production workflows, reduce human errors and prevent business interruptions.
- Increase productivity by implementing fully automated object counting. Automate and support manual tasks while lowering the risks of human errors or production issues.
- Implement a scalable and objective counting method across multiple factories and production lines. The data can be aggregated across multiple endpoints.
- Real-time object counting with cameras is fully contactless. Vision-based solutions have a small footprint, allowing the implementation without heavy modifications and with minimal disruption of existing workflows.