Paving the way for drone AI

Unmanned aerial vehicles (UAVs) have created great ease in a number of operations and in various industries. The integration of the latest technologies has enabled higher levels of reliability and a more reassuring degree of confidence in the use of drones in the air. It also allows drones to capture and identify obstacles in real time and avoid possible collisions. When deprived of computer vision, a drone only captures digital images and videos of the environment, but lacks the intelligence to understand and interact with its surroundings.

What is Computer Vision?

Computer vision is a field of artificial intelligence that trains computers to identify, interpret, and track objects in imagery and video. The technology is driven by pattern recognition. It is trained by feeding computer models of thousands to millions of images with labeled objects. This allows algorithms to build a profile (eg color, shape) for each object to then identify things in unlabeled images.

Thanks to advances in machine learning and neural networks, computer vision has made great strides in recent years and can often surpass the human eye in detecting and labeling certain objects. One of the drivers of this growth is the amount of data we generate that can be used to train computer vision models more accurately.

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

Industries are advancing with drones and computer vision

Many industries are using drones to do more work in less time. Here are some examples of industries that are turning to AI drone applications:

  • Construction
  • Agriculture
  • Remote area monitoring
  • Manufacturing and Industry
  • Smart cities
  • Security and surveillance

Construction progress monitoring

Many modern drone systems include real-time monitoring for improved safety and on-site analysis, decision-making and planning. Creating weekly progress cards is faster, easier, and cheaper with drones than it otherwise would be. Plus, they make it easier and easier for construction companies to share information with their customers, improving communication and overall efficiency.


Drones provide high-resolution images that help field workers feel less stressed. It’s simple to map and survey the field and keep an eye on the planting at all times. It also provides farmers with accurate data, allowing them to make informed decisions about pesticide application, water needs and soil health. The ability of drones to fly as high or as low as needed makes it easier for farmers to identify potential problems that the human eye at ground level cannot see. Common applications in agriculture include:

1. Soil analysis and land planning

2. Monitoring and counting of standing crops

3. Maturity monitoring and harvest timing

4. Weed detection

Smart city management

Drone-based visual AI technology can be used in smart cities to detect and respond to a variety of issues faster, earlier, and more efficiently. Cities can use drone AI to monitor and keep tabs on transportation issues such as traffic jams, accidents, and slowdowns. This can speed up first responder response times and provide crucial telemetry data to improve traffic flow and safety across the city. Drone AI for smart cities can also keep an eye out for warning signs of fires, floods, and fire hazards. This allows cities to take preventive measures or react more quickly to emergencies.

Security and surveillance

Security drones used for CCTV can perform useful and essential tasks. Drones can monitor construction sites, record aerial footage of assets, secure perimeters and deter burglaries to supplement human guards. When combined with AI technology, they can provide a real-time data feed around the clock. Drones can automatically recognize items that pose hazards and notify security of the threat, as AI renders them smarter. Additionally, they can be configured to take specific action immediately if they discover dangerous objects, weapons, perimeter intrusion or unusual activity. They can perform security operations remotely and make faster decisions in the event of an incident thanks to AI video analysis.

Manufacturing and industrial safety

Users can routinely use computer vision drones in an industrial or manufacturing scenario to continuously monitor for cracks and leaks in vital infrastructure elements including pipes, storage towers, water tanks, gas tanks, etc. These devices have the ability to monitor critical machine parts for quick and early detection of flares and overheating. To check certain requirements, they can fly through tunnels, mines, along pipes and power lines, or over facilities.

Surveillance of hazardous and remote areas

Drones are able to quickly and efficiently inspect hard-to-reach sites, survey large areas and provide the data needed to assess potentially dangerous situations. Drones enable more informed decisions during bad events due to their ability to cover large areas regardless of topography, allowing them to get closer to hazards such as high-voltage areas without exposing people to risks. A smart drone can hover quickly, spot hazards, and provide aerial photos and live video. Security personnel can then assess the level of danger and decide on the best course of action. Drones are also useful in providing full coverage of the crime scene so that officers are fully aware of the situation and assess the risk before venturing out to tackle the incident.

Annotation types for drone data

The best way to maximize the accuracy and performance of drone technology is to improve your labeled data for aerial imagery. We have listed the most common data annotation and segmentation techniques for training computer vision models on drone data.

1. 2D bounding box

Bounding boxes are rectangles drawn over objects of interest in an image. 2D bounding box annotation can be used to train your computer vision segmentation algorithms to recognize, track and avoid objects during flight.

2. 3D cuboids

2D bounding boxes are not enough to tell your drone how long, wide or deep an object is. Bringing your average bounding box into the third dimension creates a cuboid. Cuboids are essential for orienting your drone in a real environment. Image annotation for self-driving cars relies heavily on cuboids for the same reason.

3. Polygon Annotation

A real environment contains more than straight edges. To truly navigate on its own, a drone must be able to detect trees, streetlights, fences, and rooftops. All of this can involve asymmetrical and irregular shapes. Polygon annotations provide more detail than bounding boxes and cuboids in practice, more detail translates into better drone vision. The coolest part of polygon annotation is that it annotates irregularly shaped objects, providing true object detection from an aerial view.

4. Semantic segmentation

When it comes to machine learning image processing, image segmentation offers the most detail. Segmentation can be performed as a pixel-by-pixel annotation, greatly improving the visual accuracy of the environment your drone is flying over.

Especially for detail-oriented activities like geo-sensing and deforestation monitoring, semantic segmentation is frequently used.

5. Video Annotation

Video annotation for drone training helps recognize moving objects while flying through the air. Running humans, moving livestock or fast moving vehicles can only be recognized by drones, if trained with the correct training data created by the drones.

Final Thoughts

Drones and other unmanned vehicles are becoming more common. The development of drone systems with improved and refined systems has been made possible by technological advancements in areas such as blockchain, artificial intelligence and machine learning. These systems provide more safety, security and efficiency to help drone missions succeed.