WHAT IF DRONES HAD A MIND OF IT’S OWN?
Birds are seen hovering in the sky trying to reach greater heights and so does contemporary technology. Have you ever been flabbergasted witnessing something airborne which is unusual due to its metallic composition?
An unmanned bot flying on its own is in itself an enthralling piece of work and leaves you wondering how it is even within the bounds of possibility but the Computational vision in drone technology has made it realistic. Computer vision incorporated in drones plays a key role in detecting the various types of objects while hovering midair. A self-flying drone is built with various in-built computerized programming and technology like propulsion and navigation systems, GPS, sensors, cameras, programmable controllers along with equipment for automated flights.
A high-performance onboard image processing and drone neural network are used for object detection, classification, and tracking while flying in the air. The neural network in drones helps detect various types of objects ranging from vehicles, foothills, buildings, trees, objects near or on the surface of the water, to diverse terrain. Computer vision also helps detect living beings like humans, whales, ground animals and other marine mammals with high-level accuracy.
How does it make things easy for us?
The enhanced ability of such drones helps us ameliorate things and achieve tasks thought to be implausible. Such drones are devised to vanquish the hassles with traditional navigation to the hard-to-access areas.
The autonomous flying drone uses the computer vision technology to hover in the air avoiding the objects to keep moving in the right way. Apart from security surveillance and 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. The drones can be used to capture the data using the camera and sensors, which is later analyzed to extract useful information and utilized for a specific purpose. This process is known as computer vision related to automatic extraction, analysis and understanding of meaningful information through one or more images processed through computer vision technology.
What is the scope?
The Computer vision now backed with machine learning and deep learning algorithms is forging a momentous change in the drone industry. It helps algorithms comprehend from images of various objects captured while employing drones for variegated purposes.
Computer vision in drones mainly has three applications:
• Object tracking
• Self-navigation
• Obstacle detection and collision avoidance technologies.
While object tracking drone captures the real-time data during the flight, processes it with an on-board intelligence system and makes a human-independent decision from the processed data, on the other hand, self-navigation drones get pre-defined GPS coordinates about departure and destination points, with the capability to find the most optimal path and get there without manual control thanks to AI-enabled computer vision advances.
However, GPS navigation is not enough to solve the problem of collision avoidance resulting in drones or autonomous flying objects crashing into trees, buildings, high-rise poles, drones and various similar varied types of unlimited objects lying or standing in the natural environment.
So, the drone needs to be trained with a huge amount of data sets enabling it to learn and detect a wide variety of objects and obstacles both in motion and static, avoid them while moving at a high speed and further this is possible when right image annotation companies ensure providing the precisely annotated data to train the AI model for autonomous flying.
With the refurbishment in technology every day, stashing away the enhancements to be attributed to the device, this innovation is definitely not a meager and a lot of essential tasks are made facile with this solitary efficient drone.
Bibliography:
https://venturebeat.com/2020/04/08/researchers-open-source-state-of-the-art-object-tracking-ai/
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