Solve real-life visual problems in your Business
Computer Vision (CV) is a field of artificial intelligence (AI) that deals with images and pictures to solve real-life visual problems. The computer’s ability to recognize, understand, and identify digital photos or videos to automate tasks is the main goal that computer vision tasks seek to accomplish and perform successfully.
- Data Collection
- Data Preparation
- Train a model
- Analysis / Evaluation
- Serv model
- ReTrain model / Update
- Synthetics Dataset
- Externals Algorithms
- Embedded integration
- Graphic Interface application
- Controls on the Dashboard
Computer vision - fire fighting
Fire can feed us, but Fire can kill us. Forest fires are among the most serious killers of our planet. We are – humanity is obliged to fight fires by any available method. Machine learning algorithms get to our attention – Computer vision.
We have to use RGB, Thermal, IR, or any cameras for CV algorithms. We can detect and trim a fire as soon as it is discovered.
CV algorithms will be able to integrate into Drone, towers, and each day we will scan places for our safety and future.
- DATASETS for any Places
- Detection and Segmentation Learning
- Estimation Elements / Count of areas
- Prediction GPS region of Fire
- Analysis from Drone / Ground Towers
- Dashboards for Control
- AutoCalling to Fire Service
- Generation any reports to Fire Service
- Fast reaction and accuracy prediction of Fire places
Computer Vision in Smart City
Every day, in a big city, we meet face to face many problems. It’s Vehicle accidents, emergencies on the roads, lack of law and order in all parts of the city, and much more.
As a civilization, we need helpers, other eyes, and an analyzer in solving many problems.
Computer Vision algorithms will help us in solving situations and become an independent expert. It is possible to integrate algorithms in many parts of the city and combine them into one system.
- Video analysis
- Object Segmentation / Classification
- Prediction of trajectory
- Recognition of movement
- Pose statements
- Following by objects
- Visual Sign / Roads Recognition
- Drone patrolling
- Auto calling when Recognised indecent
Computer Vision in agriculture
If retrofitting farming methods with (AI) artificial intelligence were easy or cheap, everyone would have done it already. Though agriculture is estimated to be a $5 trillion industry, research suggests that output could be even higher with AI’s greater efficiency. So what is stopping Ag operations from fully deploying artificial intelligence through sensors, machine learning, and the like?
Artificial Intelligence (AI) is expanding its footprints at the ground level, making a significant impact in the world’s most vital sector — Agriculture. After the healthcare, automotive, manufacturing, and finance industries, artificial intelligence in agriculture provides cutting-edge technology for harvesting with better productivity and crop yield.
The Agriculture sector is the foundation of the world’s economy, and with the increasing population, the world will need to produce 50% more food by 2050. AI-enabled technologies can help farmers get more from the land while using resources more sustainably.
- Learning any Culture (sowing, fruits, vegetables)
- Object Detection
- Estimation Elements
- Prediction Harvest
- Analysis from Drone / Ground vehicle
- Dashboards for Control
- Generation any reports
What Kind of Include Hardware We Are Offering
We use high-speed hardware to integrate software and algorithms. Construct balanced builds of hardware for computer vision algorithms.
We integrate devices into the city and the structure of the town by all city laws.
Here is a small list of equipment based on which we assemble devices for computer vision algorithms’ operation.
We Create The Fast Computer Vision algorithms
We use flexible machine learning frameworks and tools to create computer vision algorithms. To create a product from scratch, we develop algorithms based on open source frameworks.
For product release to market – Most open-source code is rewritten for fast and balanced performance on the device.
Here is a small list of ML frameworks (lib and tools) based on which we write our Computer vision algorithms.