A Comparative Study of Different CNN Models and Transfer Learning Effect for Underwater Object Classification in Side-Scan Sonar Images

Du, Xing; Sun, Yongfu; Song, Yupeng; Sun, H.; Yang, Lei

doi:10.3390/rs15030593

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…By using the parallel computation of multi-scale convolutional kernels, the network has different "perspectives", so it can extract more scale signal features when the number of convolutional kernels is the same. This idea is also reflected in the Inception network for image recognition [29]. Figure 9 shows the structure of the multi-scale convolution module.…”

Section: Multi-scale 1d Convolutionmentioning

confidence: 90%

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Xiong,

Pan,

2023

Remote Sensing

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Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

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Section: Multi-scale 1d Convolutionmentioning

confidence: 90%

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Xiong,

Pan,

2023

Remote Sensing

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“…Focusing on the automatic recognition of side-scan sonar images of underwater objects, Du et al [30] utilized AlexNet, VGG16, GoogleNet, and ResNet to train on and predict the same dataset. While assessing these models, they emphasized prediction precision and computational economy.…”

Section: Googlenetmentioning

confidence: 99%

“…This transition was driven by a combination of the increasing complexity and volume of underwater data and the enhancement in the computational power of machine learning systems. Consequently, studies began investigating various deep learning networks' predictive abilities, focusing on their applicability and effectiveness for SSS image prediction [30]. Still, one glaring gap remained: the scarcity and quality of data available for training these deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

Du,

Sun,

Song

et al. 2023

Remote Sensing

Self Cite

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This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model’s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model’s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE ≥ ImageNet > SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models.

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“…The popular and latest CNN models are ResNet50 ( He et al, 2016 ), MobileNet ( Howard et al, 2017 ), AlexNet ( Krizhevsky, Sutskever & Hinton, 2017 ), Inception V3 ( Guidang, 2019 ), and LeNet ( Yanmei, Bo & Zhaomin, 2021 ). The models have their strengths and certain advantages in image classification ( Du et al, 2023 ). The standard CNN architectures pre-trained on well-known datasets are commonly used to accomplish transfer learning tasks ( Khalil et al, 2023a ).…”

Section: Introductionmentioning

confidence: 99%

Offloading the computational complexity of transfer learning with generic features

Safdar Ali Khan,

Husen,

Nisar

et al. 2024

PeerJ Computer Science

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Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.

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A Comparative Study of Different CNN Models and Transfer Learning Effect for Underwater Object Classification in Side-Scan Sonar Images

Cited by 17 publications

References 27 publications

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

Offloading the computational complexity of transfer learning with generic features

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