Project

In continuation of the previous project “Virtual photorealistic underwater environments for data augmentation in training machine learning methods for classification and navigation with UUVs”, it will be beneficial to include a sonar sensor in the selected UUV scenario and simulate it, as visual data can be limited by blurring at high turbidity, e.g. in port environments, at higher distances to the inspection object, or under poor lighting. The choice of sonar system must take into account specific needs and conditions in the selected underwater environment. This will allow for the collection and merging of acoustic data alongside the optical, which can contribute to a more comprehensive and versatile representation of the underwater environment. From a defense perspective, it is particularly interesting to achieve robust detection of objects in an extended working area. This can be, for example, in conditions where objects are hidden by marine fouling, lightly buried or by other masking that can be penetrated by acoustic signals.

In addition to the previous optical simulations, a sonar simulation model must therefore be developed and used. This involves a complex understanding of acoustic signal processing, as well as the unique properties of sound propagation under water, which is why it is intended to use an existing ultrasound simulator (Field-ii, developed by DTU) for the simulation itself. This step will drastically improve the possibility of a holistic simulation of the underwater environment in which the UUVs will operate.

The inclusion of sonar data provides the opportunity to train more robust and versatile machine learning models. Sonar data can be used to strengthen the models' ability for object detection and classification, especially (as mentioned) in scenarios where optical data is insufficient or unreliable, such as under high turbidity. Furthermore, the integration of different sensor data types could result in the development of a multisensor data fusion algorithm, which can improve the precision and reliability of the trained models.

Including sonar data will undoubtedly lead to technical challenges, such as the need to synchronize data from different sensors and the challenges of developing a realistic sonar simulation model. A further technical challenge will be ensuring that the machine learning algorithms can effectively merge the optical and sonar-based data to produce reliable results.