Automatic deep learning for automatic target detection of sonar images

Peng Zhang; Pengjv Zhang; Haoran Wu; Jinsong Tang

doi:10.1117/12.2675409

25 May 2023 Automatic deep learning for automatic target detection of sonar images

Peng Zhang, Pengjv Zhang, Haoran Wu, Jinsong Tang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263634 (2023) https://doi.org/10.1117/12.2675409
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

The typical deep learning detection workflow of sonar images uses a transfer-learning paradigm, i.e., to select an external optical image pre-trained classification network as the backbone for feature extraction, and then to fine-tune the entire R-CNN network on detection datasets. Though transfer learning can effectively avoid overfitting and accelerate network training, transferred models are usually too redundant and cannot achieve the optimal generalization. This paper uses Automatic Deep Learning (AutoDL) to improve the automatic target detection performance of sonar images. An improved neural architecture search algorithm is applied to automatically design the architectures of R-CNN detectors. The automatically designed architectures don’t require external optical images to pre-train and have lower requirements for R-CNN parameters and computations. Experiments on an actual measured sonar image dataset composed of small targets show that, the automatic detectors designed by the neural architecture search algorithm are better than the transfer learning detectors in terms of recall, detection precision, computations, and parameters.

Citation Download Citation

Peng Zhang, Pengjv Zhang, Haoran Wu, and Jinsong Tang "Automatic deep learning for automatic target detection of sonar images", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263634 (25 May 2023); https://doi.org/10.1117/12.2675409

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KEYWORDS

Target detection

Education and training

Deep learning

Detection and tracking algorithms

Design and modelling

Network architectures

Image classification

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