The following guest post about image annotation services for AI drones was written by Melanie Johnson, a AI and Computer Vision enthusiast who is passionate about innovation and AI-powered solutions.
The use of AI in drone technology is a must-have to automate and scale a vast amount of applications, including mapping of agricultural land or conducting equipment inspections.
By leveraging AI for image recognition, and collecting and analyzing data from sensors, AI-powered drones can inspect infrastructure projects for defects, identify projects on the ground, and even track livestock.
But with the myriad of benefits that AI-powered drones can bring to everyday life comes a need for image annotation services to support improvements in AI drone technology.
Looking to better understand what AI even means in the context of drones? Seeking out ways to improve computer vision in AI drones using image annotation services? Here’s what you need to know about image annotation services in the context of AI drones:
What is image annotation?
Image annotation goes with AI like peanut butter goes with jelly. With humans, you might not need to learn every breed of dog to know that the creature you’re looking at is, in fact, a dog.
Without AI, a computer might need to be taught about every individual species of dog. After all, a poodle can be quite different from a German Shepherd. In that process, you would have to individually tag every photo of a dog as a dog. AI automates that, able to identify similar characteristics in new photos of different breeds of dogs.
It speeds up the process. And with drones, that’s crucial.
Using image annotation in AI drones
There are a few types of image annotation methods to make the drones capable of detecting varied objects and obstacles with ease and a high level of accuracy. Some of the techniques used in image annotation for drones include:
- 2D bounding boxes for object tracking: 2D bounding boxes visualize the objects of interest in 2D either in rectangular or square shape. The drones are then able to gain an aerial visual recognition of these objects in 2D form.
- 3D cuboids for object recognition: This technique helps detect objects in three-dimensional visualization that is more precise when it comes to recognizing objects’ features such as length, width, and estimated depth. Through 3D bounding box annotation, AI-trained drones can detect these objects in 3D cuboids in real-time and are best applied in indoor object detection where accuracy and precision are key.
- Polygon annotation for objects localization: This annotation method is applied to objects that appear asymmetrical such as rooftops, swimming pools, or houses. Through localization, drones can detect these objects with ease, thanks to the polygon annotation process that annotates objects in coarse shapes.
Other image annotation techniques for AI drones include semantic segmentation, which provides enhanced visuals of objects, and video annotation for detecting moving objects.
What is the role of computer vision in drones?
So with all those methods of image annotation, why is it so important? With drones, computer vision (CV) is crucial for recognizing patterns after repeatedly going through a set of data, and then using that information to track anomalies for use cases like inspections.
Let the computer do the work in automatically extracting visual data information to identify, interpret, and track objects in photos and videos with a high level of accuracy. And that that, drones become more advanced in terms of being able to cover a large area with more visual data with a high speed and accuracy at an incredibly low cost.
This object detection process requires two things:
- high-performance onboard image processing
- drone neural networks, which mimic the human brain through a set of algorithms comprising inputs, weights, a bias or threshold, and an output.
These two requirements allow drones to identify objects, classify, and track them while in the air. The drone’s camera and sensors can capture data and analyze it to extract important information. Computer vision is applied in this process of data capture and analysis to actually interpret that information.
The challenge these days? Ensuring accuracy, which is heavily dependent on the quality of the datasets, and the accuracy of the data labeled, i.e., the training data.
Using computer vision in drones
Computer vision in drones is particularly useful in tracking objects, while at the same time self-navigating and avoiding collision by detecting obstacles. As the drone tracks objects, it captures data in real-time, processes it using the aforementioned onboard image processing system, and finally makes an independent decision without human input based on the processed data.
Even relatively low-cost drones like the $759 DJI Mini 3 Pro have some degree of computer vision for obstacle detection and avoidance. The $1,000 Skydio 2+ has enough sensors to cover all sides, thus promising to be essentially crash-proof. And of course, more advanced, enterprise focused drones tend to have even smarter sensors to make flight decisions.
When it comes to key sectors of the economy such as agriculture, drones have proven beneficial in helping farmers monitor their crops on their expansive farms through drones. The same technology is used in livestock husbandry, thanks to image annotation use cases for CV in livestock management.
The way this tech works can vary by drones. As far as the self-navigation feature goes, AI-trained drones typically have pre-defined GPS coordinates for departure and destination points. Drones then use AI to fly to their target destination using the most optimal route — independent of any manual control from a human. As far as the collision avoidance side of things, drones tend to require a massive amount of training data to help them learn.
The bottom line
From construction companies to large-scale farmers, AI-powered drones are the future in helping lower project budgets, while increasing their success thanks to accurate, actionable insights on the ground in real-time.
-By Melanie Johnson
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