Enhance your data processing with CARIS Sonar Noise Classifier, an AI-driven tool that automatically identifies and removes noise from sonar data, improving efficiency and focusing your time where it matters.
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The Sonar Noise Classifier automatically identifies noise, resulting in significantly less effort for the data processor, allowing more energy to be focused on higher-value aspects of the production chain.
The Sonar Noise Classifier was trained with numerous manually classified datasets in different geography and sensor scenarios so that it can understand typical noise patterns and real features. This allows the data processors to spend their time reviewing results rather than picking out individual clusters of noise.
The Sonar Noise Classifier is a trained Convolutional Neural Network optimized to identify noise in 3D point clouds generated by acoustic sensor platforms. All data goes through a pre- and post-processing stage to form the data into something the AI will understand: the data is rasterized into a high-resolution 3D voxel grid (zero-origin) before being transmitted to the Deep Learning Model for inference. Besides re-sampling the data into a format the AI can understand, this also has the side-benefit of completely anonymizing the data, as all spatial information is stripped during the voxelization process before transmission.
When the classified voxels are returned from the AI, the data is post-processed and the results are mapped back to individual points to continue processing. The automation results in significant less effort for the human processor, allowing more energy to be focused on other, higher-value aspects of the production chain and, ultimately, increases the availability of data.
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