Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
25,050
result(s) for
"Sonar"
Sort by:
A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition
Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.
Journal Article
Real-Time Underwater Maritime Object Detection in Side-Scan Sonar Images Based on Transformer-YOLOv5
To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.
Journal Article
Probabilistic 3D Reconstruction Using Two Sonar Devices
Three-dimensional reconstruction is a crucial technique for mapping and object-search tasks, but it is challenging in sonar imaging because of the nature of acoustics. In underwater sensing, many advanced studies have introduced approaches that have included feature-based methods and multiple imaging at different locations. However, most existing methods are prone to environmental conditions, and they are not adequate for continuous data acquisition on moving autonomous underwater vehicles (AUVs). This paper proposes a sensor fusion method for 3D reconstruction using acoustic sonar data with two sonar devices that provide complementary features. The forward-looking multibeam sonar (FLS) is an imaging sonar capable of short-range scanning with a high horizontal resolution, and the profiling sonar (PS) is capable of middle-range scanning with high reliability in vertical information. Using both sonars, which have different data acquisition planes and times, we propose a probabilistic sensor fusion method. First, we extract the region of interest from the background and develop a sonar measurement model. Thereafter, we utilize the likelihood field generated by the PS and estimate the elevation ambiguity using importance sampling. We also present the evaluation of our method in a ray-tracing-based sonar simulation environment and the generation of the pointclouds. The experimental results indicate that the proposed method can provide a better accuracy than that of the conventional method. Because of the improved accuracy of the generated pointclouds, this method can be expanded for pointcloud-based mapping and classification methods.
Journal Article
An omega‐k algorithm for multireceiver synthetic aperture sonar
In this Letter, the authors present a novel imaging algorithm for multireceiver synthetic aperture sonar (SAS). The Loffeld's bistatic formula (LBF) including quasi‐monostatic (QM) and multireceiver deformation (MD) phases is first reformulated into range‐variant and range‐invariant phases. After compensating the range‐invariant phase, the subsequent steps are the extended Stolt mapping for each bistatic SAS. Based on simulations, the presented method can also provide high‐resolution result which is similar to that of back projection (BP) algorithm. This paper presents an omega‐k algorithm for multireceiver SAS.
Journal Article
Small Target Detection Method Based on Low-Rank Sparse Matrix Factorization for Side-Scan Sonar Images
Target detection in side-scan sonar images plays a significant role in ocean engineering. However, the target images are usually severely interfered by the complex background and strong environmental noise, which makes it difficult to extract robust features from small targets and makes the target detection task quite challenging. In this paper, a novel small target detection method in sonar images is proposed based on the low-rank sparse matrix factorization. Initially, the side-scan sonar images are preprocessed so as to highlight the individual differences of the target. Then, the problems of target feature extraction and noise removal are characterized as the problem of matrix decomposition. An improved Robust Principal Component Analysis algorithm is used to extract target information, and the fast proximal gradient method is used to optimize the solution. The original sonar image is reconstructed into the low-rank background matrix, the sparse target matrix, and the noise matrix. Eventually, a morphological operation is used to filter out the noise and refine the target edges in the target matrix for improving the accuracy of target detection. Experimental results show that the proposed method not only achieves better detection performance in comparison to the conventional baseline algorithms but also performs robustly in various signal-to-clutter ratio conditions.
Journal Article
Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images
Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model’s ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.
Journal Article
Sonar image denoising based on clustering and Bayesian sparse coding
Side-scan sonar image (SSI) are often affected by a combination of multiplicative speckle noise and additive noise, which degrades image quality and hinders target recognition and scene interpretation. To address this problem, this paper proposes a denoising algorithm that integrates non-local similar block clustering with Bayesian sparse coding. The proposed method leverages cross-scale structural features and noise statistical properties of image patches, and employs a similarity metric based on the Equivalent Number of Looks (ENL) along with an improved K-means clustering algorithm to achieve accurate classification and enhance intra-class noise consistency. Subsequently, a joint training strategy is used to construct dictionaries for each cluster, and Bayesian Orthogonal Matching Pursuit (BOMP) is applied for sparse representation. This enables effective modeling and suppression of mixed noise while preserving structural details. Experimental results demonstrate that the proposed method outperforms several classical approaches in both objective metrics such as PSNR and SSIM, and in visual quality, particularly in preserving target edges and textures under severe noise conditions.
Journal Article
Near range breakdown of the phase centre approximation
Synthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centres to provide accurate micronavigation for a sensed scene. It is shown that phase centres lose coherence for near‐range scattering from large synthetic aperture sonar arrays due to the fundamentally bistatic nature of these sensors. This effect is modelled using the van Cittert‐Zernike theorem and a point‐based sonar scattering model. Reduction of the window length used in the delay estimation process can partially mitigate the loss of coherence at the expense of increased variance in the resulting delay estimates. Synthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centres to provide accurate micronavigation for a sensed scene. It is shown that phase centres lose coherence for near‐range scattering from large synthetic aperture sonar arrays due to the fundamentally bistatic nature of these sensors.
Journal Article