As technology advances, the bond between drones and artificial intelligence is tightening up, allowing us to train a high-performance model in complex and even virtual environments. This demonstrates how far we are in regards to understanding, contextualizing intricate environments, and having AI-powered drones speed through them.

When first introduced, drones were controlled manually. Oftentimes confused with Unmanned Aerial Vehicles (UAVs), what sets them apart is a drone's ability to fly autonomously without any human intervention. Today, with the integration of drones and AI, most of their operations are automated; AI drones essentially analyze high-resolution inputs from sensors and make data-driven decisions based on the input. This article's focus is solely on how AI-powered drones rely on computer vision technology to successfully gather information throughout the flight. We will be covering:

  • Understanding computer vision
  • Types of image annotation for drone training
  • AI drones fueling more industries
  • Key takeaways
computer vision in drones

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 process when the drone is flying up in the air. Computer vision for drones also enables them to capture and identify obstacles in real-time and dodge any possible collisions. When deprived of computer vision, a drone can only capture digital images and videos of the environment but lacks the intelligence to understand and interact with its surroundings.

In many cases, deep learning and machine learning algorithms are also involved with computer vision algorithms, which increases the accuracy of the prediction level. 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

We need to train drones with the right amount of data to recognize and tackle any obstacle that might come in the way of the drone (buildings, trees, flying animals, etc). The detection and darting of such objects are vital for drone technology so drones conduct safe flights and complete tasks efficiently.

Types of image annotation for drone training

The quality of the development of drone image training data often depends on both 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

Semantic segmentation is of extreme value for drone models as it demonstrates the environment in which they operate and it does so through pixel accuracy. Having said that, it becomes clearer that semantic segmentation is used for more complex tasks when precision is essential (say, annotating rooftops), as machines can cultivate a more advanced level of judgment.

semantic 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. Bounding boxes frame their target and are helpful in allocating a specific class to the target object. Without it, AI drones would have a more difficult time catching the desired object in an image and associating it with what they were originally taught.

bounding boxes drones

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 computer drones to get more work done in a shorter period of time. Here are some examples of industries that lean toward 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 can quickly capture large amounts of aerial data. 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.

construction and drones

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 them whenever they 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 have become the most optimal solution to the problem.

Farming

Drones are being used in the agriculture industry to observe 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 us 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 becoming 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.

farming and drones

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 weather conditions, performing successful safety inspections, and delivering food and supplies.

military and defense drones

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 risk assessment, minimizing downtime, and reducing operational costs without human intervention.

manufacturing and industrial drones

Smart cities

The main idea of smart cities is to have the most modern and responsive system in place. Drones with AI technology can provide a much faster and more efficient detection and reaction to issues within a building. They can also be used to observe road traffic and accidents while also providing 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.

smart cities

Security and surveillance

Drone applications ensure remarkable results in the security and surveillance sectors as they get linked from far distances and provide data for immediate action. 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, hence why drone data is of high value. Drones that are used as security cameras can be used for search and rescue missions and even detect robbery or violence, increasing the levels of safety.

security and surveillance drones

Key takeaways

As we laid out the importance and applications of AI drones in the previous paragraphs, we highlighted the importance of computer vision models along the road. Whether it is for delivering our online shopping purchases to our houses, 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 near future.

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