How to Improve Computer Vision in AI Drones Using Image Annotation Services?
The Autonomous flying drone uses the computer vision technology to hover in the air, avoiding other objects to keep moving on the instructed path. Apart from security surveillance and Aerial view monitoring, AI drone is now used by the online retail giant Amazon, to deliver the products at their customers’ doorsteps, revolutionizing the transportation and delivery system by logistics and supply chain companies.
How Computer Vision in Drone Technology Works?
Computer vision is playing a key role in detecting the various types of objects while flying in mid-air. 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.
Also Read: What Is Computer Vision: How It Works in Machine Learning and AI?
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 in detecting the living beings like humans, whales, ground animals and other marine mammals, with a high level of accuracy.
A self-flying drone is built with various in-built computerized programming and using the technology like propulsion and navigation systems, GPS, sensors and cameras, programmable controllers as well as equipment for automated flights.
The Drone is used to capture the data using the camera and sensors. This data is later analyzed to extract useful information in order to utilize for a specific purpose. This process is known as computer vision and is related to automatic extraction, analysis and understanding of meaningful information through one or more images processed through computer vision technology.
Machine Learning & Deep Learning for Computer Vision in Drones
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 drones for various purposes.
Also Read: How to Annotate Images for Deep Learning: Image Annotation Techniques
The objects are annotated to make them recognizable to drones through computer vision. A wide variety of entities are labeled to make sure drones can detect and decide its direction and control to fly safely avoiding the obstacles in the path.
Computer Vision in Drones have mainly three Applications:
- Object tracking
- Obstacle detection and collision avoidance technologies
Computer vision in drones helps to track the objects while working for self-navigation and detecting the obstacles to avoid collision With such objects. An 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, self-navigation drones get pre-defined GPS coordinates about departure and destination points. They have the capability to find the most optimal way and reach the destination without manual control, thanks to AI-enabled computer vision advances.
Similarly, GPS navigation is not enough to solve the problem of collision avoidance. Consequently, 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. It is possible when the right image annotation companies ensure providing the precisely annotated data to train the AI model for autonomous flying.
Also Read: What is the Importance of Image Annotation in AI And Machine Learning
Types of Image Annotations for Drone Training
There are various image annotation techniques, used to create the training data for drone developments. Cogito is one of the leading companies in image annotation to annotate the data with an exceptional level of accuracy to make sure drones can easily detect the varied objects.
So, let’s find out here what are the types of image annotation services available for drones and why a particular image annotation technique is useful for drones.
2D Bounding Boxes for Object Tracking
Bounding boxes outline the object of interest to visualise the 2D image of the item. It captures the object in rectangular or in a square shape to provide drone a visual recognition of objects from the aerial position. You can find here few annotated images using the bounding box techniques.
2D Bounding box image annotation can be used on still images of moving objects in the video. In a few cases, additional tags or label is added to name the object as it is known in the natural environment. This is one of the most common and simplest annotations used for creating training data for drones.
3D Cuboids Annotation for Object Recognition
This annotation technique helps to detect the object with third dimensional visualization that gives more precise recognition of objects and depicts length, width, and approximate depth of objects. To make machines understand the real-world scenario, 3D bounding box annotation is used.
Used to create a real-world scenario for self-driving cars. 3D cuboid annotation services are most helpful while developing an autonomous vehicle model. Apart from that, it also gives the precise detection of indoor objects for better in-depth object detection to computer vision-based AI models.
Polygon Annotation for Objects Localization
Similarly, polygon annotation also helps to detect the objects in asymmetrical or coarse shapes. Drones flying in the midair can detect and localize objects like houses and other structures capturing the various similar objects like rooftops, pools or trees with localization.
The most interesting part of polygon annotation is that it annotates the objects in irregular shapes providing the true detection of objects from aerial view. Apart from creating computer vision-based visualization for autonomous flying, polygon annotation is also used for autonomous driving models.
Semantic Segmentation to Avoid Obstacles
Semantic segmentation for drone training provides an enhanced visualization of objects of interest. It mainly helps to classify, localize, detect and segment multiple types of objects in the image belongs to a single class making easier for drones to classify various objects that comes on the way.
Semantic segmentation is done with pixel-wise annotation while ensuring quality and precision. In drones’ training, semantic segmentation is also used for geo-sensing and monitoring of deforestation or urbanization of open fields or agricultural lands helping the farming sector improve productivity.
Video Annotation for Moving Object Detection
Video annotation for drone training helps to recognize the moving objects while flying in midair. Humans running, livestock moving or vehicles driving fast can be only recognized by drones, if trained with right training data created through image annotation service.
Cogito provides high-quality video annotation to label the objects of interest in the video with frame-by-frame annotations making even the fast-moving objects detectable. Autonomous flying drones can recognize the wide variety of objects with accuracy.
Developing a computer vision-based AI drone needs lots of training data to visualize the various types of objects while flying in midair and to avoid collisions. To train the AI model for the drone, precisely annotated images are required for a machine-learning algorithm to detect the objects or recognize people or other actions and process the data.
Also Read: How Much Training Data is Required for Machine Learning Algorithms
Cogito is providing the autonomous flying training data solutions with a wide range of image annotations for top aerial view in the images for drone mapping and imagery making the drone training possible with highly accurate training data.