Mosaic ATM builds easily deployed custom AI applications powered by deep learning to automate object detection and classification across the entire aerospace industry.
Aerospace Computer Vision | Innovation & Automation within Reach
Computer vision is a field of artificial intelligence that trains a computer to extract information from images that would normally require human vision.
The goal of these deep learning models is not only to see but also to process and provide useful results based on the observation.
Machines can accurately classify and summarize images, identify objects, find similar images, and more. With the right application, they can react to what they see and automate a traditionally time-consuming process.
Mosaic will fit an aerospace solution to you & your business processes, not the other way around.
The air transportation and aerospace industries are adopting computer vision, yet many experience challenges.
According to the leading market research firm Research and Markets – the aviation computer vision market is expected to grow to $28B in annual revenue by 2025.
Based on the latest media hype, you might think an aviation business can point convolutional neural networks at a problem, and presto, they have machines classifying images. In reality, properly designing and deploying a functional computer vision system involves complex steps that require significant expertise to carry out successfully.
It is very challenging for a single piece of software to solve multiple use cases. Each organization’s data is quite different. A Fast R-CNN might fit a set of data inputs for one use case, while an SSD is better at identifying a different set of images. If you are going for a competitive advantage, why not build something powerful & unique to your business?
Without understanding the mechanics behind these models, you run the risk of not understanding the outputs, not being able to tune the model, or not being able to translate analytics to business terms. You’ll spend a lot of time and money on a failed project if you don’t rely on the experts.
Many companies have failed to use computer vision because they lack the required experience. Experts with significant computer vision experience can help you create adaptive and customizable models with understandable outputs and analytics that translate to business value.
Mosaic has helped several aviation customers put computer vision to work for them.
ML interprets and responds to outside environments by processing data from sensors, including cameras, lidar, and radar.
Organizations need to conduct inspections on their physical infrastructure, computer vision more accurately predicts degradation & alerts maintenance.
Aviation operators can train deep learning models to identify defects in their aircraft, improving air safety and mitigating risk.
Computer vision excels at identifying unique people using facial recognition. Low-hanging fruit includes the use cases around security, loyalty, and segmentation.
AI can be utilized to enhance or automate targeting systems, an essential development with faster missile systems that require faster reactions than humans can provide.
Manage baggage operations more efficiently and effectively by counting and capturing bags and containers as they are loaded on the aircraft.
Mosaic can build you a powerful, custom computer vision solution. Our data scientists are experts at the deep learning development needed to train these machines. Our data engineers can help gather and organize your data. Our software engineers can integrate your computer vision models with products and deploy them in a scalable infrastructure.
Buying an out-of-the-box solution might sound nice, but tuning deep learning takes substantial customization from an experienced data scientist. If a company promises you an out-of-the-box solution to classify images, they do not understand the complexity of computer vision or artificial intelligence, and you run the risk of a black box that doesn’t work.
Mosaic works with our customers where they are. We frequently coach and mentor our customers even while solving critical business problems.
New to AI? Mosaic’s proof of concept engagements are incredibly effective.
Neural what? Our intro to computer vision blog provides a gentle introduction to this deep learning technique. Mosaic focuses on the explainability of AI & machine learning; we can unpack any black box.
Using a deep learning algorithm with poor performance? Mosaic specializes in explainable AI.
Are you experimenting with neural networks but not sure how to tune them properly? In the white paper above, Mosaic explains the rapid prototyping of a diverse set of deep learning algorithms to detect airport layouts more accurately.
How do we build data-driven solutions to solve challenging problems?
Are you planning to deploy computer vision models but want to make sure you have the correct data architecture in place? Mosaic’s software engineers have been deploying algorithmic solutions since 2004. Our collaborative approach fits the AI around you, not the other way around.
Additional Mosaic Capabilities
Mosaic is designing a command and control system for Unmanned Aerial Systems to facilitate the integration of uncrewed aircraft into the current and NextGen National Airspace Systems. This solution aims to integrate state-of-the-art air traffic control automated speech recognition with robust planning and guidance.
Video demonstration of autonomous planning using speech recognition and ATC domain supervisory control of a crewless aircraft in high fidelity simulation.
This paper presents a vision-based navigation solution for uncrewed aircraft operations on airfield surfaces in GPS-denied environments. The solution combines measurements from a computer vision system and inertial sensors with an airport layout database to provide accurate real-time position determination on the airfield surface.
Computer Vision Fact Sheet
Mosaic compiled a sheet examining the rise of AI vision.
Aviation Computer Vision in 3 Steps
Finding an Image
Ingest images from various sources in real-time to be stored for analysis.
Data scientists train these deep learning models to identify thousands of labeled images. After training/validation automation can be easily deployed.
Understand the Outputs
After the artificial intelligence classifies the object, the system provides outputs back to the user or another machine.