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How Computer Vision Improves and Advances Aviation Technology

How Computer Vision Improves and Advances Aviation Technology

Shutterstock’s imagery database can reduce time and effort for those building computer vision models.

With the increasing use of computer vision in aviation, autonomous flying vehicles, and drones, the need for high-quality training data and services is more important than ever. 

These aforementioned industries use a variety of computer vision models to support vital applications in their workplaces and machinery. Computer vision technology ultimately helps aviation technology companies with tasks as far ranging as mapping, surveying, monitoring, and security. 

Here, we’ll explore computer vision’s applications throughout the aviation industry.

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What Is Computer Vision? How Does Aviation Technology Use It?

The aviation industry uses computer vision models to assist with a huge array of needs. These include: 

Object Detection

Computer vision models identify and locate objects within images, videos, or live content.

In the aviation industry, these models can be used for tasks such as identifying aircraft in flight, detecting drones, or monitoring wildlife near airports.

Image Classification

These models classify images into categories based on their content.

In the aviation technology industry, image classification models can be used for tasks such as identifying different types of aircraft, recognizing different types of terrain, or detecting specific weather patterns.

Semantic Segmentation

These models segment images into regions based on their content. In aviation, these models can be used for tasks such as identifying runway markings, distinguishing between different types of vegetation, or identifying different types of buildings and infrastructure.

Optical Character Recognition (OCR)

OCR models recognize text within images or video. When used within the aviation industry, these specific models can read aircraft registration numbers.

They can also identify signs and other text-based information on the ground.

Clearly, computer vision models play an important role in the aviation industry, as they support a wide range of applications related to safety, security, and operations.

License these images via Nick Starichenko, dibrova, Denis Belitsky, and Denis Belitsky.

Aviation Technology Pain Points When Using Computer Vision Models

Creating computer vision models for aviation technology can involve several challenges and pain points, including:

Data Quality and Quantity

One of the primary challenges in creating computer vision models is obtaining high-quality training data. In many industries—not just aviation—data can be scarce or difficult to obtain.

In aviation, though, it can be particularly difficult to determine the best data for specialized applications, like object detection for aircrafts. The quality of the data may vary due to factors such as weather conditions or lighting.

All of these factors can impact the accuracy of the models.

Model Complexity

Computer vision models often require complex algorithms and architectures, which can be difficult to design and optimize.

Developing accurate models that can handle complex scenes with multiple moving objects—such as airport runways—require extensive computational resources and expertise.

Integration with Existing Systems

Incorporating AI models into existing aviation systems can be challenging, particularly when it comes to real-time processing and integration with other sensors and data sources.

Companies may need to invest in additional hardware and software infrastructure to support these models, which can be costly and time-consuming.

Regulatory Compliance

The aviation industry is heavily regulated. Companies must ensure that any computer vision models they develop comply with existing safety and privacy regulations.

This often involves extensive testing and validation to ensure that the models are accurate, reliable, and secure.

Addressing these challenges requires a combination of technical expertise, access to high-quality data, and a deep understanding of the aviation industry and its regulatory environment.

Despite these challenges, there are ways to reduce the pain and improve the time to market for computer vision solutions. Training your models on diverse, high-quality, and expansive data is the only way to move forward.

License these images via MAGNIFIER, Jaromir Chalabala, Sven Hansche, and petrmalinak.

Shutterstock Provides AI and Computer Vision Solutions for the Aviation Industry 

Shutterstock offers large, diverse datasets and metadata enrichment services. Both of these solutions can fuel accurate and reliable computer vision models, meeting the mission-critical needs of the aviation and autonomous flight industries. 

Shutterstock has:

  • The largest and most diverse dataset for computer vision training data in the aviation technology, autonomous flying vehicles, and drone industries 
  • High-quality labeling and metadata enrichment services 
  • Expansive, diverse, and accurately labeled datasets that provides fair representation and mitigates bias risks 

In addition, Shutterstock’s customer service team provides support throughout the data curation and delivery process. We ensure that aviation-related clients can have the resources they need to succeed. 

If you’re looking to learn more about computer vision technology, no matter your industry, download our white paper. It details how Shutterstock is at the forefront of developing responsible and ethical AI technologies.

In it, we examine the growth of computer vision applications across industries, current legal discussions, and ethical concerns. It also presents a four-point framework for creating trustworthy computer vision and ensuring maximum benefits for organizations and individuals alike.

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