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.
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 features ready-made recognition modules and offers deep learning consultancy to develop custom video analysis routines. This service fits organizations who have innovative ideas and want to use the skills of a dedicated company.

THE GATEKEEPER AS AN AI DEVICE

Deep learning models require a vast amount of processing power. AVUTEC rounds the circle to a final and successful deployment. We optimize networks and algorithms to work with embedded hardware and have developed an deep learning camera system, stacked with processing power to achieve the best performance.
AVUTEC delivers not only a full-grown Computer Vision platform, our embedded AI camera system completes our portfolio with a highly integrated, edge computing IoT computer vision sensor, the Gatekeeper.

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.

3. Training

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.

4. 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.

4. Testing

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.