Through constant research and development, AVUTEC evolved from exclusively recognizing number plates to recognizing all kinds of objects. With our knowledge and tools in video content analysis and image classification or detection, AVUTEC now offers deep learning consultancy to develop any intelligent solution.
AVUTEC’s deep learning consultancy
Deep learning, machine learning and computer vision technologies are conquering the world by storm. We can now heat up our house from a distance, are introduced to new movies or music based on our previous choices and soon autonomous driving cars will be ordinary.
Since smart technologies are essential for companies to keep up in a world of constant progress, AVUTEC provides deep learning consultancy that contains video content analysis and image classification or detection. This service is suitable for organizations who have innovative ideas but lack the skills or capacity to create this by themselves.
The Gatekeeper as an AI device
A tailored AI system can be integrated with CortexFramework, AVUTEC’s modular platform for building any type of computer vision solution. A dedicated Axon – a CortexFramework building block – is developed for this purpose.
The AI device that matches CortexFramework best is the Gatekeeper. After all, it was purposely designed to work seamlessly with this computer vision platform. This embedded sensor is known for its automatic number plate recognition, but it can be used for all image content analysis purposes. Specially developed hardware like the Gatekeeper is the best choice when it comes to high-quality deep learning.
The benefits of AVUTEC's deep learning consultancy
Tailored deep learning solutions are integrated with the modular platform of CortexFramework, AVUTEC’s tool to build computer vision solutions
The Gatekeeper is an embedded IoT camera, it can be used for all video content analysis and image classification or detection
Customized deep learning models
With our knowledge and available networks, AVUTEC develops video content analysis for all possible requirements
A strategy for quality
Setting clear goals, defining the use of technology and the training and testing of deep learning models ensure high quality products
The deep learning procedure
AVUTEC’s deep learning service starts by defining the exact goals for the model. After targets are set the process consists of four consecutive steps: Data collection, data preparation, training and testing.
1. Data collection
The deep learning consultancy for video analysis always starts by defining clear goals. These goals are a starting point for collecting data and measuring the performance of the final model. Once the targets are set, the data collection can start. When data is collected, the quality of the data is a prediction for the quality of the results. The data needs to be representative of the actual circumstances the application will be used in. Poor data is equal to poor performance.
2. Data preparation
The next phase in the deep learning process is the preparation of the data. All visual material needs to have a predefined format with the same size and the same aspect ratio. Next to that, every image must be labelled, clean, consistent and accurate. Achieving this is a labor-intensive task, since it needs to be done with great precision.
During the collecting and preparing of data, a strategy for the use of technology is made. This strategy defines the deep learning models and techniques that will be used, depending on the processing powers of the IoT device. Once the strategy is established, the actual training of a model can begin.
Testing the trained model in the field is the final phase of the deep learning consultancy. The application is released in beta version and a test protocol is applied, which results in generated reports that display the performance of the trained model. As soon as targets are met, it is time to release the final version.