scholarly journals Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1823
Author(s):  
Qiang Ge ◽  
Fengxue Ruan ◽  
Baojun Qiao ◽  
Qian Zhang ◽  
Xianyu Zuo ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Object Classification ◽  
Fine Tuning ◽  
Visual Fatigue ◽  
Style Transfer ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Sonar Images

Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”.

A field-programmable gate array system for sonar image recognition based on convolutional neural network

2020 ◽  
pp. 095965182093934
Author(s):  
Chong Wang ◽  
Yu Jiang ◽  
Kai Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Field Programmable Gate Array ◽  
Oil And Gas ◽  
Sonar Image ◽  
Field Programmable ◽  
Sonar Images ◽  
Gate Array

Subsea pipeline is the safest, most reliable, and most economical way to transport oil and gas from an offshore platform to an onshore terminal. However, the pipelines may rupture under the harsh working environment, causing oil and gas leakage. This calls for a proper device and method to detect the state of subsea pipelines in a timely and precise manner. The autonomous underwater vehicle carrying side-scan sonar offers a desirable way for target detection in the complex environment under the sea. As a result, this article combines the field-programmable gate array, featuring high throughput, low energy consumption and a high degree of parallelism, and the convolutional neural network into a sonar image recognition system. First, a training set was constructed by screening and splitting the sonar images collected by sensors, and labeled one by one. Next, the convolutional neural network model was trained by the set on the workstation platform. The trained model was integrated into the field-programmable gate array system and applied to recognize actual datasets. The recognition results were compared with those of the workstation platform. The comparison shows that the computational precision of the designed field-programmable gate array system based on convolutional neural network is equivalent to that of the workstation platform; however, the recognition time of the designed system can be saved by more than 77%, and its energy consumption can also be saved by more than 96.67%. Therefore, our system basically satisfies our demand for energy-efficient, real-time, and accurate recognition of sonar images.

scholarly journals Sonar image recognition of underwater target based on convolutional neural network

2021 ◽  
Vol 39 (2) ◽  
pp. 285-291
Author(s):  
Leilei Jin ◽  
Hong LIANG ◽  
Changsheng Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Target Recognition ◽  
Saliency Detection ◽  
Image Features ◽  
Feature Maps ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

Underwater sonar image classification using adaptive weights convolutional neural network

2019 ◽  
Vol 146 ◽  
pp. 145-154 ◽  
Author(s):  
Xingmei Wang ◽  
Jia Jiao ◽  
Jingwei Yin ◽  
Wensheng Zhao ◽  
Xiao Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Adaptive Weights ◽  
Sonar Image

scholarly journals Sonar image recognition based on fine-tuned convolutional neural network

2019 ◽  
Vol 283 ◽  
pp. 04012 ◽  
Author(s):  
Zhaotong Zhu ◽  
Youfeng Hu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Recognition Rate ◽  
Fine Tuning ◽  
Learning Method ◽  
Scaled Model ◽  
Training Time ◽  
Sonar Image ◽  
Few Data

To solve the problem of sonar image recognition, a sonar image recognition method based on fine-tuned Convolutional Neural Network (CNN) is proposed in this paper. With the development of deep learning, CNN shows impressive performance in image recognition. However, massive data is needed to train a CNN from beginning. Through fine-tuning pre-trained CNN can help us training CNN from relatively high starting points, based on those pre-trained CNNs, only few data is needed to retrain a CNN which focus on sonar image recognition. A scaled model experiment shows that based on the architecture of AlexNet, compared with the traditional learning method, the transfer learning method can achieve higher recognition accurate rate of 95.81% and less training time. Moreover, this paper also compared 6 pre-trained networks, among those networks, VGG16 can achieve the highest recognition rate of 99.48%.

scholarly journals Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization

2020 ◽  
Vol 10 (23) ◽  
pp. 8494
Author(s):  
Vili Podgorelec ◽  
Špela Pečnik ◽  
Grega Vrbančič
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Parameter Optimization ◽  
Classification Performance ◽  
Training Data ◽  
Fine Tuning ◽  
American Football ◽  
Massive Scale

With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations.

scholarly journals A Multi-Domain Collaborative Transfer Learning Method with Multi-Scale Repeated Attention Mechanism for Underwater Side-Scan Sonar Image Classification

2022 ◽  
Vol 14 (2) ◽  
pp. 355
Author(s):  
Zhen Cheng ◽  
Guanying Huo ◽  
Haisen Li
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Side Scan Sonar ◽  
Multi Scale ◽  
Sonar Image ◽  
Optical Images

Due to the strong speckle noise caused by the seabed reverberation which makes it difficult to extract discriminating and noiseless features of a target, recognition and classification of underwater targets using side-scan sonar (SSS) images is a big challenge. Moreover, unlike classification of optical images which can use a large dataset to train the classifier, classification of SSS images usually has to exploit a very small dataset for training, which may cause classifier overfitting. Compared with traditional feature extraction methods using descriptors—such as Haar, SIFT, and LBP—deep learning-based methods are more powerful in capturing discriminating features. After training on a large optical dataset, e.g., ImageNet, direct fine-tuning method brings improvement to the sonar image classification using a small-size SSS image dataset. However, due to the different statistical characteristics between optical images and sonar images, transfer learning methods—e.g., fine-tuning—lack cross-domain adaptability, and therefore cannot achieve very satisfactory results. In this paper, a multi-domain collaborative transfer learning (MDCTL) method with multi-scale repeated attention mechanism (MSRAM) is proposed for improving the accuracy of underwater sonar image classification. In the MDCTL method, low-level characteristic similarity between SSS images and synthetic aperture radar (SAR) images, and high-level representation similarity between SSS images and optical images are used together to enhance the feature extraction ability of the deep learning model. Using different characteristics of multi-domain data to efficiently capture useful features for the sonar image classification, MDCTL offers a new way for transfer learning. MSRAM is used to effectively combine multi-scale features to make the proposed model pay more attention to the shape details of the target excluding the noise. Experimental results of classification show that, in using multi-domain data sets, the proposed method is more stable with an overall accuracy of 99.21%, bringing an improvement of 4.54% compared with the fine-tuned VGG19. Results given by diverse visualization methods also demonstrate that the method is more powerful in feature representation by using the MDCTL and MSRAM.

scholarly journals Recognition and Quantification of Dual Phase Titanium Alloy Microstructures Using Convolutional Neural Networks

2020 ◽  
Vol 321 ◽  
pp. 11084
Author(s):  
Ryan Noraas ◽  
Vasisht Venkatesh ◽  
Luke Rettberg ◽  
Nagendra Somanath
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Titanium Alloy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Phase Image ◽  
Dense Layer ◽  
Dual Phase

Recent advances in machine learning and image recognition tools/methods are being used to address fundamental challenges in materials engineering, such as the automated extraction of statistical information from dual phase titanium alloy microstructure images to support rapid engineering decision making. Initially, this work was performed by extracting dense layer outputs from a pretrained convolutional neural network (CNN), running the high dimensional image vectors through a principal component analysis, and fitting a logistic regression model for image classification. Kfold cross validation results reported a mean validation accuracy of 83% over 19 different material pedigrees. Furthermore, it was shown that fine-tuning the pre-trained network was able to improve image classification accuracy by nearly 10% over the baseline. These image classification models were then used to determine and justify statistically equivalent representative volume elements (SERVE). Lastly, a convolutional neural network was trained and validated to make quantitative predictions from a synthetic and real, two-phase image datasets. This paper explores the application of convolutional neural networks for microstructure analysis in the context of aerospace engineering and material quality.

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