Our Computer Vision already deployed use case and POCs
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A. Our Project samples
B. Our Proof of Concept samples
C. IoT Project samples
Our Computer Vision Use Case-1:
Revolutionizing Warehouse Operations with Advanced Computer Vision
The image showcases a robotic arm in action, picking up boxes of various sizes from different conveyors and dropping them on specific down racks. The computer vision model developed by Saturnbits provides detailed insights into this process:
Robotic Arm Actions
- The model identifies the size or type of the picked box.
- It determines the target Down Rack where the robotic arm is going to drop the box.
- It identifies from which conveyor the robotic arm has picked the box.
- It provides information about the current status of the target rack in terms of space available.
- It also provides the current timestamp of these actions.
Down Rack Status
- The model displays the space available in terms of percentage on each Down Rack.
- It calculates and displays the number of boxes which can be further dropped in respective Down Racks.
Additional Insights
- Around the robotic arm area, it displays the total weight it has picked in terms of percentage.
- The model also predicts and displays the type of rotation (clockwise or anticlockwise) that the robotic arm is going to perform based on the type of box picked and from which conveyor it was picked.
These insights provided by Saturnbits computer vision model are crucial for understanding and optimizing the automation setup. They allow for real-time monitoring and prediction of actions, contributing to efficient and effective operations.
**Note:** Above Computer Vision part is developed by Saturnbits and complete automation setup including all hardware is developed and installed by ‘Omniautomation’. Please visit Omniautomation website or click here Omniautomation website to know more about automation setup and its hardware part.
Our Computer Vision Use Case-2:
Enhancing Pizza Processing with Computer Vision
Overview
Computer vision technique is applied to the pizza processing automation unit where it can detect and predict various points. Indirectly, computer vision technique is applied in the food processing industry to get the best result, for testing purposes, and for analyzing purposes.
Points Displayed Using Computer Vision Model and Techniques
- Toppings level is predicted by using our own generated computer vision model.
- Pizza taste levels including Non-spicy, medium spicy, spicy, and hot & spicy are identified using our computer vision model.
- Pizza size is identified so that pizza size can be analyzed.
- On the basis of pizza base size, the quantity of dough used to make pizza is also detected.
- The current timestamp of these actions is also displayed.
**Note:** Above Computer Vision part is developed by Saturnbits and complete automation setup including all hardware is developed and installed by ‘intralox’. Please visit intralox website or click here Intralox website to know more about automation setup and its hardware part.
Our Computer Vision Use Case-3:
Revolutionizing Supermarkets with Custom Computer Vision
Overview
Our custom-trained computer vision model is able to accurately detect and display expected items from images with more than 99% accuracy. This technology has numerous applications in supermarkets, allowing for real-time monitoring and prediction of stock levels, shelf or rack status, and product demand.
Benefits of Computer Vision in Supermarkets
- Real-time stock monitoring: Our computer vision model can detect the number of items remaining on shelves or racks, providing up-to-date information about stock levels.
- Shelf or rack status: The model can also provide information about the status of shelves or racks, such as whether they are full, partially full, or empty.
- Stock predictions: By analyzing historical data, our computer vision model can predict future stock levels and demand for specific products.
- Datewise prediction analysis: Our model can also provide datewise prediction analysis of a particular product, allowing for more accurate forecasting and planning.
- Improved customer satisfaction: By providing accurate and up-to-date information about stock levels and product availability, our computer vision model can help improve customer satisfaction.
- Reduced waste and costs: By accurately predicting demand and stock levels, our computer vision model can help reduce waste and costs associated with overstocking or understocking products.