Side-scan sonar based on convolutional neural network for target recognition classification of submarine aircraft wreckage image

Xu Liu; Hanhao Zhu; Qile Wang; Jiahui Wang; Zhigang Chai

doi:10.1117/12.3019703

23 January 2024 Side-scan sonar based on convolutional neural network for target recognition classification of submarine aircraft wreckage image

Xu Liu, Hanhao Zhu, Qile Wang, Jiahui Wang, Zhigang Chai

Author Affiliations +

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 1297803 (2024) https://doi.org/10.1117/12.3019703
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

The manual interpretation of traditional side-scan sonar images has problems such as solid subjectivity, low efficiency, high labor cost, and large time consumption, and the machine learning method requires manual feature selection, which lacks adaptability and robustness. In this paper, we introduce a convolutional neural network method, which can automatically learn features from side-scan sonar submarine aircraft wreckage images and complete classification recognition. Using 48 side-scan sonar submarine aircraft images in the SeabedObjects (ship and aircraft) dataset after preprocessing 1686 images, the convolutional neural network model is trained and tested. The results show that the trained CNN model can accurately identify and classify the side-scan sonar submarine aircraft wreck images with an accuracy of 98.85%, which is highly efficient, accurate and robust, and can effectively improve the recognition and classification level of side-scan sonar submarine aircraft wreckage images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xu Liu, Hanhao Zhu, Qile Wang, Jiahui Wang, and Zhigang Chai "Side-scan sonar based on convolutional neural network for target recognition classification of submarine aircraft wreckage image", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 1297803 (23 January 2024); https://doi.org/10.1117/12.3019703

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