3D Forward-Looking Sonar Recognition for Real-Time Target Detection
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Solution Overview
Problem
Existing three-dimensional forward-looking sonar systems generate large amounts of data that are difficult for human operators to interpret in real-time, limiting their application for safe navigation and target detection in water.
Innovation Solution
Implementing machine learning algorithms, specifically convolutional neural networks, to process and classify three-dimensional sonar data, incorporating additional data sources for training to improve accuracy and reduce false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional forward-looking sonar systems generate large amounts of data for improved detection accuracy, then target detection precision is improved, but data interpretation difficulty increases
Solution Approach 1:
A machine learning model serves as an intermediary between the raw sonar data and the operator. The model processes the complex three-dimensional sonar data and outputs simplified target detections and classifications, making the data interpretable for navigation decisions without requiring operators to manually analyze the complex raw data.
Solution Approach 2:
The patent replaces manual mechanical data interpretation by human operators with an automated machine learning system. The ML model automatically processes, detects, and classifies targets in the sonar data, substituting human cognitive processing with computational algorithms that can handle the complexity of three-dimensional sonar data.
2Ease of operation
If traditional detection algorithms are used for simplicity, then ease of operation is maintained, but detection accuracy and reliability deteriorate
Solution Approach 1:
The machine learning model performs self-service by automatically processing and interpreting the sonar data without requiring operator intervention for each detection. The model continuously learns from training data and can autonomously adapt to different sonar environments, maintaining ease of operation while improving reliability through consistent, repeatable detection performance.
3Measurement precision
If machine learning models with more training data and features are used, then detection accuracy is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the detection task into separate processing stages: data collection, machine learning processing, and output generation. The ML model is trained on segmented training data from multiple sources (bathymetric surveys, radar data, AIS data) and processes three-dimensional sonar data through dedicated computational layers, allowing complexity to be managed through modular architecture rather than monolithic processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, precise, and accurate detection and classification of seafloor and in-water targets, enhancing navigation safety and usability of sonar systems.
Implementation Method 1
Sonar systems use sound propagation to map the seafloor and identify objects under the surface of the water
Implementation Method 2
The input to the algorithm includes a 3-dimensional array of backscatter strength from the sonar sensor
Data Source
AI summary
Machine learning algorithms can interpret three-dimensional sonar data to provide more precise and accurate determination of seafloor depths and in-water target detection and classification. The models apply architectures for interpreting volumetric data to three-dimensional forward-looking sonar data. A baseline set of training data is generated using traditional image and signal processing techniques, and used to train and evaluate a machine learning model, which is further improved by additional inputs to improve both seafloor and in-water target detection.