DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification

Irfan, Muhammad; Zheng, Jiangbin; Ali, Shahid; Iqbal, Muhammad; Masood, Zafar; Hamid, Umar Zakir Abdul

doi:10.1016/j.eswa.2021.115270

Cited by 133 publications

(54 citation statements)

References 29 publications

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“…Only the identification of a single vessel within a range of 2 km from the hydrophone was used to generate the data, and the background noise recordings were added from a distinct source. Table 9 shows a summary of the best results obtained in the present work (first block), against the results reported in [41].…”

Section: Discussionmentioning

confidence: 81%

“…An accuracy value of around 87% was reported as a result of the application of a bio-inspired cochlea model preprocessing filter to a CNN-based classification [40]. In addition, a comparison of various deep learning methods was conducted in [41] using an analogous set of ONC raw data that was used in the present paper. However, it was reported that 77.53% was the highest accuracy obtained in that work.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Mel-log spectrograms, delta, and delta-delta features were also used as acoustic filters in a ship detection task using a CNN, where high accuracy in the detection and localisation of vessels was reported [37]. Other studies in the literature also successfully applied filters to the DL inputs, showing a consistent improvement in underwater audio classification tasks [38]- [41]. This strongly suggests that, although CNNs are capable of learning distinct filters in their convolutional layers, there may be insufficient training time or data for the network to converge on the best solution.…”

Section: Related Workmentioning

confidence: 99%

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An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data

et al. 2022

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The illegal exploitation of protected marine environments has consistently threatened the biodiversity and economic development of coastal regions. Extensive monitoring in these -often remoteareas is challenging. Machine learning methods are useful in object detection and classification tasks and have the potential to underpin techniques for the development of robust monitoring systems to overcome this problem. However, development is hindered due to the limited number of publicly available labelled and curated datasets. Furthermore, there are relatively few open-source state-of-the-art methods to be used for evaluation. This paper presents an investigation of automated classification methods using underwater acoustic signals to infer the presence and type of vessels navigating in coastal regions. Various combinations of deep convolutional neural network architectures, and preprocessing filter layers, were evaluated using a new dataset based on a subset of the extensive open-source Ocean Networks Canada hydrophone data. Tests were conducted in which VGGNet and ResNet networks were applied to classify the input data. The data was preprocessed using either Constant Q Transform (CQT), Gammatone, Mel spectrogram, or a combination of these filters. With over 97% accuracy, using all three preprocessing representations simultaneously yielded the most reliable result. However, high accuracies of 94.95% were achieved using CQT as the preprocessing filter for a ResNet-based convolutional neural network, providing a trade-off between model complexity and accuracy; a result that is more than 10% higher than previously reported approaches. This more accurate classifier for underwater acoustics could be used as a reliable autonomous monitoring system in maritime environments.INDEX TERMS deep learning, hydrophones, marine environment, ship type, sound

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data

et al. 2022

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“…Table 4 reveals that the conventional DSC block [14] adopted in the area with fewer input channels. In this block, convolutions are divided into depthwise and pointwise convolutions [15]. Assuming that the number of input channels in the network is M, the number of output channels to be obtained after convolution is N, and the convolution kernel size is K. The parameter S after ordinary convolution is obtained by Equation (8):…”

Section: Proposed Methodsmentioning

confidence: 99%

Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

2022

IET Radar Sonar & Navi

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Formation recognition is a significant focus of maritime target recognition. Automatic formation extraction and recognition facilitate autonomous decision-making. However, few studies have explored formation extraction prior to recognition. This paper introduces a density-based spatial clustering of applications with noise (DBSCAN) method based on Gaussian kernel to extract formation targets. On this basis, a depthwise separable convolutional neural network (DSCNN) method is proposed for formation recognition. A track simulation system is established to form a track dataset containing three different proportions of clutter, and the formation extraction method is examined using track dataset. Subsequently, the image dataset with eight different types of formation is formulated, on the basis of various detection errors, the DSCNN method for formation recognition is compared with several typical deep learning methods. As exposed in experimental results, the DBSCAN method based on Gaussian kernel can guarantee accurate extraction of formation targets subject to different proportions of clutter. Hence, it is greatly robust and capable of effective formation extraction. Under different radar detection errors, the formation recognition accuracy of DSCNN is 91.5%-99.5%, which achieves performance improvement by up to 12.5% compared with other deep learning methods. The combination of DBSCAN and DSCNN can well realise formation extraction and recognition with different proportions of clutter in tracks and various radar detection errors.

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“…Our ship radiated noise signal data comes from DeepShip [43]. Our interference signal data were collected in the South China Sea.…”

Section: Datamentioning

confidence: 99%

Time-Domain Anti-Interference Method for Ship Radiated Noise Signal

Duan

Shen

Haiyan

2022

EURASIP J. Adv. Signal Process.

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Ship radiated noise signal is one of the important ways to detect and identify ships, and emission of interference noise to shield its own radiated noise signal is a common countermeasure. In this paper, we try to use the idea of signal enhancement to enhance the ship radiated noise signal with extremely low signal-to-noise ratio, so as to achieve anti-explosive signal interference. We propose a signal enhancement deep learning model to enhance the ship radiated noise signal by learning a mask in the temporal domain. Our approach is an encoder–decoder structure with U-net. U-net consists of 1d-conv with skip connection. In order to improve the learning ability of the model, we directly connect the U-net in series. In order to improve the learning ability of the model’s time series information. The Transformer attention mechanism is adopted to make the model have the ability to learn temporal information. We propose a combine Loss function for scale-invariant source-to-noise ratio and mean squared error in time-domain. Finally, we use the actual collected data to conduct experiments. It is verified that our algorithm can effectively improve the signal-to-noise ratio of the ship radiated noise signal to 2 dB under the extremely low signal-to-noise ratio of − 20 dB to − 25 dB.

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DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification

Cited by 133 publications

References 29 publications

An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data

An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data

Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

Time-Domain Anti-Interference Method for Ship Radiated Noise Signal

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