The Deep-learning technique detects multiple ship targets better than conventional networks.
n underwater acoustics, deep learning is gaining traction in improving sonar systems to detect ships and submarines in distress or in restricted waters. However, noise interference from the complex marine environment becomes a challenge when attempting to detect targeted ship-radiated sounds.
In the Journal of the Acoustical Society of America, published by the Acoustical Society of America through AIP Publishing, researchers in China and the United States explore an attention-based deep neural network (ABNN) to tackle this problem.
"We found the ABNN was highly accurate in a target recognition, exceeding a conventional deep neural network, particularly when using limited single-target data to detect multiple targets," co-author Qunyan Ren said.
Deep learning is a machine-learning method that uses artificial neural networks inspired by the human brain to recognize patterns. Each layer of artificial neurons, or nodes, learns a distinct set of features based on the information contained in the previous layer.
ABNN uses an attention module to mimic elements in the cognitive process that enable us to focus on the most important parts of an image, language, or other pattern and tune out the rest. This is accomplished by adding more weight to certain nodes to enhance specific pattern elements in the machine-learning process.
Incorporating an ABNN system in sonar equipment for targeted ship detection, the researchers tested two ships in a shallow, 135-square-mile area of the South China Sea. They compared their results with a typical deep neural network (DNN). Radar and other equipment were used to determine more than 17 interfering vessels in the experimental area.
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