Introducing the Brand New QiandaoEar22 Dataset for Specific Ship Identification Using Ship-Radiated Noise

TL;DR

This paper introduces QiandaoEar22, a comprehensive underwater acoustic dataset for ship identification, and demonstrates its effectiveness with deep learning models achieving high recognition accuracy, thereby advancing underwater target recognition research.

Contribution

The paper provides the first multi-target ship-radiated noise dataset for underwater recognition and establishes baseline results using deep learning, facilitating future research in the field.

Findings

Deep learning models achieved up to 97.78% accuracy in ship identification.

Spectrum and MFCC features with DenseNet classifier performed best.

The dataset enables benchmarking and development of new underwater acoustic recognition methods.

Abstract

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To ntackle this issue, we release QiandaoEar22 \textemdash an underwater acoustic multi-target dataset, which can be download on https://ieee-dataport.org/documents/qiandaoear22. This dataset encompasses 9 hours and 28 minutes of real-world ship-radiated noise data and 21 hours and 58 minutes of background noise data. We demonstrate the availability of QiandaoEar22 by conducting an experiment of identifying specific ship from the multiple targets. Taking different features as the input and six deep learning networks as classifier, we evaluate the baseline performance of different methods. The experimental results reveal that identifying the specific target of UUV from others can achieve the optimal recognition accuracy of 97.78\%, and we find using spectrum and MFCC as feature inputs and DenseNet as the classifier can achieve better recognition performance. Our work not only establishes a benchmark for the dataset but helps the further development of innovative methods for the tasks of underwater acoustic target detection (UATD) and underwater acoustic target recognition(UATR).