How AI Based Drone Works: Artificial Intelligence Drone Use Cases - B-AIM PICK SELECTS

September 17, 2020

 

 

Autonomous flying machines or drones use the computer vision technology to hover in the air avoiding the objects to keep moving on the right path. And now artificial intelligence (AI) is used in drones to make this flying machine smarter.

From security surveillance to aerial view monitoring, AI drone is now used by online retail giant Amazon to deliver the products at customer’s doorstep revolutionizing the transportation and delivery system by logistics and supply chain companies.

 

Computer Vision in Drone Technology

Computer vision is playing a key role in detecting the various types of objects while flying in midair. A high-performance on board image processing and a drone neural network are used for object detection, classification, and tracking while flying into the air.

The neural network in drones helps to detect the various types of objects like vehicles, foothills, buildings, trees, objects on or near the surface of the water, as well as diverse terrain.

Computer vision also helps detect livening beings like humans, whales, ground animals and other marine mammals with a high level of accuracy.

 

How Drone Technology Works?

A self-flying drone is built with various in-built with computerized programming and using the technology like propulsion and navigation systems, GPS, sensors and cameras, programmable controllers as well as equipment for automated flights.

 

Video: Drone Detecting Object Through Computer Vision

Drone used to capture the data using the camera and sensors, which is later analyzed to extract useful information to utilize for a specific purpose.

This process is known as computer vision and related to the automatic extraction, analysis and understanding of meaningful information through one or more images processed through computer vision technology.

 

Deep Machine Learning in Object Detection & Drone Navigation

Computer vision now backed with machine learning and deep learning algorithms is making a drastic change in the drone industry. It helps algorithms to learn from captured images of various objects that come while using the drones for various purposes.

The objects are annotated to make it recognizable to drones through computer vision. And a wide variety of entities are labeled to make sure drone can detect and decide its direction and control to fly safely avoiding the obstacles in the path.

 

Applications of Computer Vision in Drones:

  • Object tracking

  • Self-navigation

  • Obstacle detection and collision avoidance technologies

Computer vision in drones helps to track the objects while working for self-navigation and detect the obstacles to avoid a collision from such objects.

While object tracking drone captures the real-time data during the flight, processes it with an on-board intelligence system in real-time, and makes a human-independent decision based on the processed data.

While on the other hand, in self-navigation drones get pre-defined GPS coordinates about departure and destination points, with the capability to find the most optimal way and get there without manual control thanks to AI-enabled computer vision advances.

Similarly, GPS navigation is not enough to solve the problem of collision avoidance. Resulting, drones or autonomous flying objects crash into trees, buildings, high-rise poles, drones and various similar varied types of unlimited objects lying or standing in the natural environment.

Here, the drone needs to be trained with a huge amount of data sets to make it learn and detect a wide variety of objects and obstacles, both static and in motion, and avoid them when moving at a high speed.

And it is possible when right image annotation companies ensure providing the precisely annotated data to train the AI model for autonomous flying.

 

 

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