Volumetric measurement, or volumetry, is a critical process in many industries, from manufacturing to logistics. It involves determining the three-dimensional space occupied by a material or object, an essential step for quality control, inventory management, and efficient resource utilization.
In industrial environments, accurate volumetric measurements can mean the difference between profit and loss, as well as ensuring compliance with regulations and customer specifications. Traditionally, these measurements relied on conventional surveying methods or basic sensors, which can be time-consuming, prone to human error, and limited in their ability to handle abundant material or complex shapes.
These challenges have long been a hindrance for industries seeking greater efficiency and precision. However, the advent of computer vision and artificial intelligence technologies is revolutionizing the field of volumetric calculation, offering new solutions to old problems.
Computer vision systems, equipped with high-resolution cameras and sophisticated algorithms, can now capture and analyze three-dimensional data with unprecedented speed and accuracy. When combined with the learning and adaptive capabilities of AI, these intelligent volumetric measurement systems can handle the complexities of real-world industrial environments, where objects can vary in shape, size, and orientation.
This technological synergy is opening new possibilities for automating and optimizing volumetric measurements, promising to increase productivity, reduce waste, and improve overall operational efficiency across various industries.
How Smart Volumetric Measurement Works
Three technologies are essential for implementing a computer vision-based volumetric measurement system: LIDAR, stereoscopic cameras, and edge artificial intelligence.
LIDAR (Light Detection and Ranging) works like a high-tech version of the echolocation used by bats, but with light instead of sound. A device fires rapid pulses of laser light, which hit objects in the environment and bounce back to the LIDAR sensor. The device then measures how long it takes for each pulse of light to return.
Since we know the speed of light, the device can calculate the exact distance to each point where the light bounced. By repeating this process millions of times per second and in different directions, the LIDAR system creates a detailed 3D map of its surroundings.
Stereoscopic cameras are image capture devices that simulate human binocular vision, allowing the creation of three-dimensional images. These devices typically have two or more lenses, each with its own image sensor, positioned at a distance from each other similar to that of human eyes. They capture two slightly different images of the same object or scene, one for each “eye.” When these images are combined, either by special visualization techniques or digital processing, they create the illusion of depth and three-dimensionality.
However, these two technologies are only responsible for data collection. The “magic” happens when the sensors are connected to an edge-based artificial intelligence system.
This type of system processes information in real-time, performing volumetric calculations to estimate the volume of a pile of soybeans or ore, for example, and presenting the results intuitively in the user interface. The processing is done locally, close to the point of capture. Therefore, we say it is an “edge” system, located on the boundary between a local network and the internet.
This allows for a reduction in traffic between the sensors and the platform where the data is processed, as only the results of the observations are transmitted. It also increases privacy and security, as sensitive information, such as camera feeds, does not need to travel over the internet.
Uses and Benefits of Smart Volumetry
Smart volumetric measurement can be applied to a wide variety of use cases in the industrial segment, all bringing benefits such as more rigorous control, optimized resource management, and greater operational agility. Among them, we have:
- Warehouse volumetric measurement: for accurate calculation of the volume available for material storage, avoiding unpleasant surprises with lack of space during times of high demand.
- Measurement of grain stock in warehouses and silos: to optimize sales, reduce manual measurement time, and facilitate the issuance of credit certificates, as well as ensure operational safety, avoid overcrowding, and increase revenue.
- Ore volumetric measurement in yards: to control production, manage waste efficiently, control milling speed, reduce equipment degradation, and improve logistical planning.
- Sugarcane bagasse volumetric measurement: to assist in making economic decisions about selling it or using for energy generation, automate processes, promote sustainability, and ensure accurate measurements.
- Quantification of loads in trucks and train wagons: to ensure safety, efficiency, and regulatory compliance in transportation, avoiding losses, reducing tire and rail wear, and optimizing loading.
Adding Value with Artificial Intelligence
We can go a step beyond computer vision by adopting systems such as dtLabs’s AIOS, which adds artificial intelligence features. With this, it is possible to implement solutions that would be unthinkable in legacy systems, and will increase the efficiency of your processes.
One of them is the separation of materials combined with volumetric measurement. This allows for the detection of impurities and the precise determination of volume by type of material. Artificial intelligence analyzes the images captured by the camera, identifying and classifying different materials and ensuring that only pure materials are accounted for and used.
Artificial intelligence also enables automated inventory calculation and stock forecasting. By regularly measuring the variation in the volume of materials over time, such as grains in silos or sugarcane in yards, systems like AIOS can predict changes in material levels. The measurement results are combined with historical and current data and analyzed to identify patterns and trends, providing accurate forecasts.
This allows for more efficient planning, helping to avoid both scarcity and excess inventory. This also facilitates logistics management, ensuring that materials are available when needed and avoiding costs with excessive storage.
Conclusion
In this article, we presented the main advantages of adopting new technologies for smart volumetric measurement, based on our field experience, our partners, and the most common cases already implemented with our customers.
From the precise measurement of grain stocks to the quantification of truck loads, through waste control and impurity detection, volumetric measurement with computer vision and artificial intelligence provides more efficient and economical management, in a more accessible and effective way.
The combination of volumetric measurement and artificial intelligence not only enhances the accuracy of measurements but also allows companies to make informed decisions, improve operational efficiency, and ensure the quality of materials, providing more rigorous control and optimized management.
