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Computer Vision for Drones and UAVs

Computer Vision for Drones and UAVs

Computer vision solutions for image and object recognition involve algorithms and deep learning models that enable computers to interpret and understand visual information from the surrounding environment. These solutions enable unmanned and autonomous systems to identify and classify objects by processing images or video streams in real-time. By recognizing and interpreting visual data, these technologies allow drones, robotics and USVs to navigate complex environments safely, make informed decisions, and execute tasks autonomously.

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Overview Computer Vision for Drones and UAVs

By Staff Writer Last updated: October 17th, 2024

What is Computer Vision?

Computer vision is an area of artificial intelligence focused on enabling machines to interpret and derive meaningful information from visual data, such as images and video streams. It involves the creation of artificial systems that can intelligently analyze visual content to identify patterns, objects, or scenes, that make it possible for devices to understand and interact with their environment through sight.

The technology uses sophisticated algorithms and models to process images and extract features, allowing the AI to recognize and classify objects, detect motion, and reconstruct three-dimensional spaces from two-dimensional inputs.

computer vision system lookout plus by Greenroom Robotics — Lookout+ vision-based AI system by Greenroom Robotics to provide real-time detection, classification, and tracking of maritime hazards

Unmanned & Autonomous Applications

Computer vision solutions are used for complex use cases by unmanned and autonomous platforms such as drones, unmanned aerial systems (UAS) and unmanned surface vessels (USVs). A common application of the technology is in detect-and-avoid systems, allowing objects in flight, obstacles on the ground, or hazards on or in the water to be spotted and manoeuvred around.

Primary uses by autonomous systems include the detection, recognition and classification of objects, patterns, and images for safe navigation and collision avoidance. Systems can also be deployed during drone search and rescue (SAR) and surveillance operations to relieve the cognitive burden on operators.

Additional applications in drones and unmanned aerial systems include the detection of forest fires, target detection in counter-drone systems, defect spotting in the automated inspection of pipelines, power lines and wind turbines, and the identification of missing persons or casualties in SAR and other first responder missions.

Computer Vision Airborne Imaging Payload PT 6 by Overwatch Imaging — PT-6 airborne imaging payload by Overwatch Imaging featuring built-in AI and comper vision software that can automatically scan for small objects in vast search areas.

Computer vision is also used as part of SLAM (simultaneous localization and mapping), in which an autonomous vehicle can create a map of a previously unknown area and navigate around it.

Computer Vision Solution Awarion AI — Awarion® AI Computer Vision Solution by Charles River Analytics that detects the presence of whales, ships, and other objects using EO/IR video streams

Object Recognition Sensors

Sensors and payloads used to acquire images for computer vision may include RGB and thermal cameras, video cameras, and LiDAR scanners. The data from these may be augmented by other sources such as GPS receivers and environmental sensors.

Image Recognition & Analysis

Computer vision solutions include software for image recognition and may be performed on board the drone or unmanned vehicle, or the data may be streamed to a cloud server or other computing facility for offsite analysis, thus saving onboard processing power.

A variety of analysis methods may be used on the images, including artificial intelligence (AI)-powered object detection and classification. The velocity of objects picked up by imaging sensors enables these systems to estimate the velocity of detected objects and facilitate effective tracking and real-time decision-making.