Search Results (15)

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

Underwater acoustic target recognition (UATR) technology plays a significant role in marine exploration, resource development, and national defense security. To address the limitations of existing methods in computational efficiency and recognition performance, this paper proposes an improved WS-ViT model based on Vision Transformers (ViTs). By introducing the Wavelet Transform Convolution (WTConv) module and the Simplified Linear Attention (SLAttention) module, WS-ViT can effectively extract spatiotemporal complex features, enhance classification accuracy, and significantly reduce computational costs. The model is validated using the ShipsEar dataset, and the results demonstrate that WS-ViT significantly outperforms ResNet18, VGG16, and the classical ViT model in classification accuracy, with improvements of 7.3%, 4.9%, and 2.1%, respectively. Additionally, its training efficiency is improved by 28.4% compared to ViT. This study demonstrates that WS-ViT not only enhances UATR performance but also maintains computational efficiency, providing an innovative solution for efficient and accurate underwater acoustic signal processing. Full article

Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, traditional feature engineering (FE) has been enhanced to interface matching–feature engineering (IM-FE). This approach requires considerable effort in feature design, larger sample duration, or a higher upper limit of frequency. In this context, this paper proposes a one-dimensional network design for underwater acoustic target recognition (UATR-ND1D), only combined with fast Fourier transform (FFT), which can effectively alleviate the problem of IM-FE. This method is abbreviated as FFT-UATR-ND1D. FFT-UATR-ND1D was applied to the design of a one-dimensional network, named ResNet1D. Experiments were conducted on two mainstream datasets, using ResNet1D in 4320 and 360 tests, respectively. The lightweight model ResNet1D_S, with only 0.17 M parameters and 3.4 M floating point operations (FLOPs), achieved average accuracies were 97.2% and 95.20%. The larger model, ResNet1D_B, with 2.1 M parameters and 5.0 M FLOPs, both reached optimal accuracies, 98.81% and 98.42%, respectively. Compared to existing methods, those with similar parameter sizes performed 3–5% worse than the methods proposed in this paper. Additionally, methods achieving similar recognition rates require more parameters of 1 to 2 orders of magnitude and FLOPs. Full article

Underwater Acoustic Target Recognition (UATR) is critical to maritime traffic management and ocean monitoring. However, underwater acoustic analysis is fraught with difficulties. The underwater environment is highly complex, with ambient noise, variable water conditions (such as temperature and salinity), and multi-path propagation of acoustic signals. These factors make it challenging to accurately acquire and analyze target features. Traditional UATR methods struggle with feature fusion representations and model generalization. This study introduces a novel high-dimensional feature fusion method, CM3F, grounded in signal analysis and brain-like features, and integrates it with the Boundary-Aware Hybrid Transformer Network (BAHTNet), a deep-learning architecture tailored for UATR. BAHTNet comprises CBCARM and XCAT modules, leveraging a Kan network for classification and a large-margin aware focal (LMF) loss function for predictive losses. Experimental results on real-world datasets demonstrate the model’s robust generalization capabilities, achieving 99.8% accuracy on the ShipsEar dataset and 94.57% accuracy on the Deepship dataset. These findings underscore the potential of BAHTNet to significantly improve UATR performance. Full article

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition. Full article

Although recent data-driven Underwater Acoustic Target Recognition (UATR) methods have played a dominant role in marine acoustics, they suffer from complex ocean environments and rather small datasets. To tackle such challenges, researchers have resorted to transfer learning in an effort to fulfill UATR tasks. However, existing pre-trained models are trained on audio speech data, and are not suitable for underwater acoustic data. Therefore, it is necessary to make further optimization on the basis of these models to make them suitable for the UATR task. Here, we propose a novel UATR framework called Attention Layer Supplement Integration (ALSI), which integrates large pre-trained neural networks with customized attention modules for acoustic. Specifically, the ALSI model consists of two important modules, namely Scale ResNet and Residual Hybrid Attention Fusion (RHAF). First, the Scale ResNet module takes the Constant-Q transform feature as input to obtain relatively important frequency information. Next, RHAF takes the temporal feature extracted by wav2vec 2.0 and the frequency feature extracted by Scale ResNet as input and aims to better integrate the time–frequency features with the temporal feature by using the attention mechanism. The RHAF module can help wav2vec 2.0, which is trained on speech data, to better adapt to underwater acoustic data. Finally, the experiments on the ShipsEar dataset demonstrated that our model can achieve recognition accuracy of $96.39\%$. In conclusion, extensive experiments confirm the effectiveness of our model on the UATR task. Full article

Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the unique features of ship radiation noise. This study proposes a novel cross-domain contrastive learning-based few-shot (CDCF) method for UATR to alleviate overfitting issues. This approach leverages self-supervised training on both source and target domains to facilitate rapid adaptation to the target domain. Additionally, a base contrastive module is introduced. Positive and negative sample pairs are generated through data augmentation, and the similarity in the corresponding frequency bands of feature embedding is utilized to learn fine-grained features of ship radiation noise, thereby expanding the scope of knowledge in the source domain. We evaluate the performance of CDCF in diverse scenarios on ShipsEar and DeepShip datasets. The experimental results indicate that in cross-domain environments, the model achieves accuracy rates of 56.71%, 73.02%, and 76.93% for 1-shot, 3-shot, and 5-shot scenarios, respectively, outperforming other FSL methods. Moreover, the model demonstrates outstanding performance in noisy environments. Full article

In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models. Full article

Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and immediacy of the target recognition system. In this paper, an underwater acoustic target recognition model based on a deep residual attention convolutional neural network called DRACNN is proposed, whose input is the time-domain signal of the underwater acoustic targets radiated noise. In this model, convolutional blocks with attention to the mechanisms are used to focus on and extract deep features of the target, and residual networks are used to improve the stability of the network training. On the full ShipsEar dataset, the recognition accuracy of the DRACNN model is 97.1%, which is 2.2% higher than the resnet-18 model with an approximately equal number of parameters as this model. With similar recognition accuracies, the DRACNN model parameters are 1/36th and 1/10th of the AResNet model and UTAR-Transformer model, respectively, and the floating-point operations are 1/292nd and 1/46th of the two models, respectively. Finally, the DRACNN model pre-trained on the ShipsEar dataset was migrated to the DeepShip dataset and achieved recognition accuracy of 89.2%. The experimental results illustrate that the DRACNN model has excellent generalization ability and is suitable for a micro-UATR system. Full article

Underwater acoustic target recognition (UATR) technology has been implemented widely in the fields of marine biodiversity detection, marine search and rescue, and seabed mapping, providing an essential basis for human marine economic and military activities. With the rapid development of machine-learning-based technology in the acoustics field, these methods receive wide attention and display a potential impact on UATR problems. This paper reviews current UATR methods based on machine learning. We focus mostly, but not solely, on the recognition of target-radiated noise from passive sonar. First, we provide an overview of the underwater acoustic acquisition and recognition process and briefly introduce the classical acoustic signal feature extraction methods. In this paper, recognition methods for UATR are classified based on the machine learning algorithms used as UATR technologies using statistical learning methods, UATR methods based on deep learning models, and transfer learning and data augmentation technologies for UATR. Finally, the challenges of UATR based on the machine learning method are summarized and directions for UATR development in the future are put forward. Full article

Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data and ship working conditions. As the marine environment is very complex, UATR relies heavily on feature engineering, and manually extracted features are occasionally ineffective in the statistical model. In this paper, an end-to-end model of UATR based on a convolutional neural network and attention mechanism is proposed. Using raw time domain data as input, the network model combines residual neural networks and densely connected convolutional neural networks to take full advantage of both. Based on this, a channel attention mechanism and a temporal attention mechanism are added to extract the information in the channel dimension and the temporal dimension. After testing the measured four types of ship-radiated noise dataset in experiments, the results show that the proposed method achieves the highest correct recognition rate of 97.69% under different working conditions and outperforms other deep learning methods. Full article

The acquisition of target data for underwater acoustic target recognition (UATR) is difficult and costly. Although deep neural networks (DNN) have been used in UATR, and some achievements have been made, the performance is not satisfactory when recognizing underwater targets with different Doppler shifts, signal-to-noise ratios (SNR), and interferences. On the basis of this, this paper proposed a Siamese network with two identical one-dimensional convolutional neural networks (1D-CNN) that recognize the detection of envelope modulation on noise (DEMON) spectra of underwater target-radiated noise. The parameters of underwater samples were diverse, but the states of the collected samples were very homogeneous. Traditional underwater target recognition uses multi-state samples to train the network, which is costly. This article trained the network using samples from a single state. The expectation was to be able to identify samples with different parameters. Datasets of targets with different Doppler shifts, SNRs, and interferences were designed to evaluate the generalization performance of the proposed Siamese network. The experimental results showed that when recognizing samples with Doppler shifts, the classification accuracy of the proposed network reached 95.3%. For SNRs, the classification accuracy reached 85.5%. The outstanding generalization ability of the proposed model shows that it is suitable for practical engineering applications. Full article

This paper focuses on the automatic target recognition (ATR) method based on ship-radiated noise and proposes an underwater acoustic target recognition (UATR) method based on ResNet. In the proposed method, a multi-window spectral analysis (MWSA) method is used to solve the difficulty that the traditional time–frequency (T–F) analysis method has in extracting multiple signal characteristics simultaneously. MWSA generates spectrograms with different T–F resolutions through multiple window processing to provide input for the classifier. Because of the insufficient number of ship-radiated noise samples, a conditional deep convolutional generative adversarial network (cDCGAN) model was designed for high-quality data augmentation. Experimental results on real ship-radiated noise show that the proposed UATR method has good classification performance. Full article

Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods. Full article

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR. Full article