As technology advances, the bond between drones and AI is tightening up, allowing to train a high-performance model in complex and even virtual environments. This is to demonstrate how far we are in regards to understanding, contextualizing intricate environments, and having AI drones speed through them. When first introduced, drones were controlled manually. Today with the integration of AI, most of their operations are automated; drones essentially analyze high-resolution inputs from sensors and make data-driven decisions based on the input. This article’s focus is solely how AI-powered drones rely on computer vision technology to successfully gather information throughout the flight:
- Understanding computer vision
- Types of image annotation for drone training
- AI drones fueling more industries
- Key takeaways
Understanding computer vision
Computer vision plays a crucial role in the drone industry, as it is in charge of the classification process, object detection, and tracking when the drone is flying up in the air. It also enables drones to capture and identify obstacles in real-time and dodge possible collisions. When deprived of computer vision, a drone only captures digital images and videos of the environment but doesn’t have the intelligence to understand and interact with its surroundings.
In many cases, machine learning and deep learning are also involved with computer vision algorithms, which increases the prediction level accuracy. Here are the main applications of computer vision in drones:
Autonomous drone navigation
When it comes to determining the endpoints and departures, self-navigation drones have the competence to discover the optimal, barrier-free way to safely arrive at a particular destination all by themselves due to AI-enabled technology.
Obstacle recognition and tracking
A model has to be fed the right amount of data to recognize and tackle any obstacle that might come in the way of the drone (buildings, trees, flying animals, and so on). The detection and darting of such objects are vital for drones to conduct safe flights and complete tasks efficiently.
Types of image annotation for drone training
The quality of the training data for drone development often depends on the right annotation toolset used and the skills of the annotator. Below we’ll cover some of the most commonly used annotation techniques for AI drones:
- Semantic segmentation
- Instance segmentation
- Bounding boxes
Semantic segmentation
Semantic segmentation is of extreme value for drone models as it explains to them the environment in which they operate and does so through pixel accuracy. Meaning semantic segmentation is used for more complex tasks when precision is important (say, annotating rooftops), as machines can cultivate a more advanced level of judgment.
Instance segmentation
The difference between semantic and instance segmentation is that semantic segmentation identifies the object category, while instance segmentation gives unique labels to the instances. The latter detects and outlines objects of interest while allocating a label to every single pixel. A sample use case of instance segmentation is counting items for harvest monitoring (when the background is not important).
Bounding boxes
Bounding boxes are essentially rectangles used for object detection and tracking (of vehicles, for example) in computer vision. They frame their target and are helpful in allocating a specific class to the target object. Without bounding boxes, it would be harder for the AI drone to catch the desired object in an image and associate it with what they were originally taught.
AI drones fueling more industries
By now, we’ve learned that computer vision elevates the applications of drones from multiple perspectives. What we haven’t explored yet is how computer vision in AI drones contributes to the development of novel industries; the usage of AI has created a list of brand new opportunities for effective and flexible production as more high-quality data becomes available.
Many sectors resort to drones to get more work done in a shorter period of time. Here are some examples of industries that lean towards AI drone applications:
- Construction
- Power transmission line tracking
- Farming
- Military and defense
- Manufacturing and industrial
- Smart cities
- Security and surveillance
Construction
Drones are being used in the construction industry for surveying, project closeout, and inspection purposes, as they have downward-facing, sometimes even front-facing sensors and the ability to capture a large amount of aerial data in a short period of time. Images and videos that are captured by drones can be used to track the construction of a building and provide timely updates. Another beneficial aspect of having AI drones in construction companies is their ability to map and examine the landscape of a building in a matter of a few short minutes. The same operation would take days to finish if only humans were involved.
Power transmission line tracking
Red Eléctrica, the sole transmission agent and operator (TSO) of the Spanish electricity system, tracks kilometers of high-voltage power transmission lines across the border and detects whenever those are blocked by obstacles, such as trees. To guarantee the correct functioning of the electricity system, it has to be continually free from potential hazards. AI-enabled drones became the most optimal solution to the problem.
Farming
Drones are even being used in the agriculture industry to observe the crop conditions from above and make improved choices when it comes to fertilization, harvesting, and planting. They are also being used as agriculture and environmental monitoring tools as they allow to scan and comprehend changes in bigger properties, ranches, forests, and other land areas. Agricultural drone technologies are also being used to track livestock movement and detect forest fires, which is more and more relevant these days. All of these features not only save time and money for the farmers but also improve productivity levels in the agriculture industry.
Military and defense
AI drones are heavily used in the military and defense sector to develop unmanned weapons to fight opponents during wars. AI military drones are also used for guarding borders, ensuring security, tracking the weather conditions, performing successful safety inspections, and delivering food and supplies.
Manufacturing and industrial: [Client use case]
As for manufacturing and industrial applications, workers can use computer vision drones to constantly watch out for any possible cracks and leakages in the pipes, water tanks, storage towers, etc. By paying close attention to these components, they can easily and immediately detect overheating and flares and take respective measures to avoid them in the future. Drones can even go through mines and tunnels and fly over specific facilities to observe detailed conditions.
Percepto, one of SuperAnnotate's dedicated clients, is a leading provider of autonomous drone-in-a-box solutions. Their drones are used to monitor and secure industrial sites and core infrastructure. SuperAnnotate's AI-based software has provided Percepto with real-time insights, improving the risk assessment, minimizing downtime, and reducing operational costs without human intervention.
Smart cities
The main idea of smart cities is to have the most modern and responsive systems in place. Drones with AI technology can provide a much faster and more effective detection and reaction to issues within a building. They can also be used to observe road traffic and accidents and provide a quicker response to emergencies. Additionally, AI-controlled drones are helpful in urban management as they provide mapping and urban landscape design, which, in turn, allows architects and civil engineers to come up with the most practical plan layouts.
Security and surveillance
In the security and surveillance sectors, drone applications ensure remarkable results to get linked from far distances and provide data for immediate actions. AI-assisted drones have the ability to collect HD images and videos on a 24-hour basis, catch phone calls, determine license plate information, and track down GPS locations. Drones that are used as security cameras can even detect robbery or violence and, hence, increase levels of safety.
Key takeaways
As we laid out the importance and applications of AI drones in the previous paragraphs, we highlighted the importance of computer vision along the road. Whether it is for delivering our online shopping purchases to our house, detecting wildfires in the forests, or observing street traffic, AI drones are enhancing our daily lives. With the right algorithms and data, they can surely have more influence and benefits in the future.
