Research on underwater target localization technology based on active and passive electric field

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.

For the detection of shallow underwater targets by the hyperspectrum, the information of the target is carried in the water-leaving radiation. However, the differences between the water-leaving radiation of the underwater target and the ocean background radiation are not significant, and the spectrum of the water-leaving radiation is sensitive to the changes of the marine environment. Therefore, taking the spectrum as the feature is not stable for hyperspectral detection of the underwater target. In this paper, we propose a parameter feature extraction method for hyperspectral detection of the shallow underwater target. The presence of an underwater target would change the optical properties of seawater in a local place. Based on that, a quasi-analytical model of the water-leaving radiation is adopted to inverse the properties of the marine environment from the radiated spectrum. Then the inversed water depth and bottom reference reflectance are taken as the features. Simulation results show that the samples which belong to the water-leaving radiation of underwater target have higher inversed depth and reference reflectance than that belong to the ocean background radiation. Furthermore, these two kinds of samples can be separated significantly and stably in a 2-dimension feature space.

Active sonar systems are used in the detection and localization of underwater targets. While traditional approaches use pulsed active sonar (PAS) to transmit short bursts, high duty cycle (HDC) sonar systems have been the focus of recent research and can overcome the shortcomings of PAS. Since HDC sonar systems transmit a long pulse train waveform, we must address sub-pulse interference issues, which requires multiple orthogonal sub-pulses and generalized sinusoidal frequency modulated (GSFM) pulses are suitable for this purpose. Unfortunately, conventional GSFM pulse train design methods do not generate an adequate number of orthogonal sub-pulses. Therefore, we propose an improved GSFM pulse train waveform design approach for overcoming the limitations of these conventional methods. The proposed method is organized into two parts, the first focused on the assessment of the auto-correlation, and the second focused on the optimizing the orthogonality between sub-pulses. Cost function is also carefully designed for this purpose. From simulation experiments, we found that the proposed method was able to produce 30 orthogonal sub-pulses and an optimized waveform that demonstrated better detection performance than conventional waveforms under most conditions. We also verified the performance of the proposed method via actual sea experiments, with analysis showing superior performance matching that of simulations. An analysis on the bandwidth parameter K is also conducted and it is found K = 1.25 to be the best option.

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.

In view of the problem of difficult feature extraction and target missed detection, propose an improved transformer based underwater target detection algorithm. The proposed algorithm improves the linear embedding module of the Transformer to supplement the linear embedding information of small objects and builds the local relationship model of each layer feeling field, in order to achieve end-to-end accurate detection. An experiment on URPU underwater target detection data set is conducted. Compared with Swim Transformer, the proposed algorithm detection accuracy is 77.4%, 1. 5%, detection speed is 7 frame/s, 35.6%, the number is 30.4 MB, and compression is 84.1%. Experimental results have shown that the improved algorithm improves the performance of underwater small target detection

The detection and recognition of underwater preset structure is an important topic in the detection of potential safety hazards in underwater engineering. At present, the detection of underwater preset structure is mainly based on multi-beam sounding technology. In this paper, the submarine pipeline is taken as the research object, and a new detection method based on 3D real-time imaging sonar technology is proposed, which realizes the underwater environment acoustic high-precision real-time target detection and fine target analysis. First of all, the underwater target detection data is obtained by using 3D imaging sonar, high-precision integrated navigation, tide gauge, CTD and other equipment. Secondly, the attitude correction, sound velocity correction and depth compensation of 3D imaging data is carried out, and the bilateral filtering combined with threshold filtering is used to filter the data. Finally, combined with advanced point cloud data processing algorithms and edge data extraction based on eigenvalues, accurate identification, positioning and status analysis of submarine pipelines are achieved. The results of practical engineering detection show that this method has the advantages of high resolution, real-time imaging and excellent imaging quality. The detection image is stereoscopic and clear, which can provide more detailed description of underwater target contour. The data processing and analysis method adopted in this paper can track and identify the submarine pipeline quickly and accurately, and detect the areas with potential safety hazards such as exposed or suspended pipelines, which provides scientific basis for the timely management of pipelines. This paper provides a new detection method for submarine pipeline detection, which has applicability and popularizing value.

Underwater target detection is an indispensable part of marine environmental engineering and a fast and accurate method of detecting underwater targets is essential. Although many target detection algorithms have achieved great accuracy in daily scenes, there are issues of low-quality images due to the complex underwater environment, which makes applying these deep learning algorithms directly to process underwater target detection tasks difficult. In this paper, we presented an algorithm for underwater target detection based on improved You Only Look Once (YOLO) v4 in response to the underwater environment. First, we developed a new convolution module and network structure. Second, a new intersection over union loss was defined to substitute the original loss function. Finally, we integrated some other useful strategies to achieve more improvement, such as adding one more prediction head to detect targets of varying sizes, integrating the channel attention into the network, utilizing K-means++ to cluster anchor box, and utilizing different activation functions. The experimental results indicate that, in comparison with YOLOv4, our proposed algorithm improved the average accuracy of the underwater dataset detection by 10.9%, achieving 91.1%, with a detection speed of 58.1 frames per second. Therefore, compared to other mainstream target detection algorithms, it is superior and feasible for applications in intricate underwater environments.

The underwater environment of the sea bottom is complex, and it is easy to be affected by light, underwater impurities and shooting angle. Because of the dense and mutual occlusion of underwater targets, the collected images are not clear, fuzzy and easy to be resolved, which affects the accuracy of target detection and recognition. Aiming at the above problems, this paper proposes an improved YOLOV5 method for underwater seafood target detection. Firstly, the C3 module of high-level features is fused with swin transformer to create the C3STR module, and the improved C3STR module can enlarge the receptive field of the network, obtain the global information better and increase the ability of feature extraction, the DC3 module is constructed by adding convolution to the low-level feature C3 module, which improves the model detection ability with a little more computation. Finally, the network feature fusion module is improved, which integrates the deep-level information with the shallow-level information, the combination of semantic information and position information can improve the precision of model recognition and enhance the ability of target detection. The experimental results show that the detection accuracy of the improved YOLOv5 algorithm is increased to 88.3%, which is 2% higher than that of the original YOLOv5 algorithm.

The most popular and crucial piece of equipment for underwater target detection right now is side-scan sonar, which primarily plays a crucial role in underwater operations. Unfortunately, in general, side-scan sonar images have low resolution and suffer from significant noise interference, making target recognition with these images highly challenging. In this paper, we employ deep learning techniques to recognize underwater targets using side scan sonar imagery. This paper begins by outlining the current state of underwater target detection research as well as briefly describing its importance and historical context. The imaging process of side-scan sonar pictures is studied in greater depth, and it is determined that noise on sonar images is caused principally by the combined effect of external and internal noise. This causes extra noise in sonar pictures, lowering image quality and affecting target identification performance. Proper sonar image pre-processing findings can provide more consistent target detection help. After then, the YOLOv5 target detection algorithm is investigated and improved. By performing several sets of comparison trials, the updated technique improves the accuracy and speed of side-scan sonar image target recognition and finally overcomes the issues of missed detection, false detection, and low accuracy of overlapping tiny object recognition.

We consider underwater target detection by using a novel active, self-contained and rapidly deployable underwater inflatable co-prime sonar array (UICSA). In particular, we measure the received signal strength (RSS) and angle-of-arrival (AoA) of acoustic signals reflected by an underwater target. Measurements from different positions of the transmitter with respect to the UICSA are organized in a three mode real-valued tensor. Then, the conformity of each entry with respect to all other data points in the tensor is calculated based on recursively refined calculations of $L_{1}$ -norm tensor subspaces. Conformity values are then used for the detection of an underwater target. We evaluate the data conformity of RSS and AoA recordings acquired from the testbed deployment of a seven-element UICSA prototype and three underwater acoustic transmitters at Florida Atlantic University. We show for the first time that conformity evaluation over multi-modal data measurements can accurately detect the presence of an underwater target.

Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5.

The underwater target detection is affected by image blurring caused by suspended particles in water bodies and light scattering effects. To tackle this issue, this paper proposes a reparameterized feature enhancement and fusion network for underwater blur object recognition (REFNet). First, this paper proposes the reparameterized feature enhancement and gathering (REG) module, which is designed to enhance the performance of the backbone network. This module integrates the concepts of reparameterization and global response normalization to enhance the network’s feature extraction capabilities, addressing the challenge of feature extraction posed by image blurriness. Next, this paper proposes the cross-channel information fusion (CIF) module to enhance the neck network. This module combines detailed information from shallow features with semantic information from deeper layers, mitigating the loss of image detail caused by blurring. Additionally, this paper replace the CIoU loss function with the Shape-IoU loss function improves target localization accuracy, addressing the difficulty in accurately locating bounding boxes in blurry images. Experimental results indicate that REFNet achieves superior performance compared to state-of-the-art methods, as evidenced by higher mAP scores on the underwater robot professional competitionand detection underwater objects datasets. REFNet surpasses YOLOv8 by approximately 1.5% in $mAP_{50:95}$ on the URPC dataset and by about 1.3% on the DUO dataset. This enhancement is achieved without significantly increasing the model’s parameters or computational load. This approach enhances the precision of target detection in challenging underwater environments.

Target of underwater environments classification and distinguish using active sonar is very complex signal processing work, therefore, many research institutions in the world study the classification and recognition methods of underwater small targets using active sonar.The recognition methods of static underwater small targets can be roughly divided into imaging method and echo method. The imaging method is based on high-frequency narrow beam to obtain the acoustic image of the target and its shadow while suppressing the reverberation interference, so as to realize the classification and recognition of the target. Imaging sonar has high working frequency, limited sonar range, and can not effectively identify false targets with similar geometry. Bright spot model and resonance scattering theory are important theoretical basis of target echo method. The two theories agree that the echo is the result of the interaction between the incident signal and the underwater target, which must contain the characteristics of the target, so it is feasible to detect the target by analyzing the characteristics of the echo signal. However, in the actual underwater target detection process, due to the weak target echo signal, complex seabed environment and target geometry, uncertain sound propagation conditions and so on, the target recognition based on echo method still has great challenges.

A real-time magnetic localization method based on unscented particle filter (UPF) is proposed to solve the problem for real-time localization of underwater non-cooperative magnetic targets. The state space model of non-cooperative magnetic targets is set up according to its motion characteristic, and the state is estimated in real time by UPF. In order to improve the observability of the system, the particle state is constrained and magnetic moment is inverted by the least square method. The simulation results and ferromagnetic object localization experimental results show that the method is good in localization precision and real-time performance, which can be applied to the real-time magnetic localization in near field.

Underwater imaging plays an important role in sonar detection. Compressive sensing can achieve high-precision airborne sound source localization with a small computational effort, but underwater target localization and ranging have not been fully achieved. This study proposes a metamaterial-based compressive sensing imaging method for underwater sound source localization and target imaging. To overcome the poor wave manipulation performance caused by the impedance difference between water and the structure, a strong coupling interaction between the flexible structure and the medium is introduced to reduce reflection and scattering, dramatically improving the acoustic signal transmission amplitude via the resonance mechanism. Analytical and numerical results show that this device can achieve directional irrelevant modulation of incident acoustic waves in different directions. Besides, the multi-information fusion compressive sensing method and dual-sensor joint compressive sensing method can reconstruct the sound source information from the received mixed signals. Experimental results show that the metamaterial-based compressive sensing imaging device realizes the perception and localization of a single sound source underwater in multiple directions. The metamaterial-based hydroacoustic imaging device eliminates the dependence on large-caliber arrays and complex control systems, thus opening the way to establish a sonar system with low cost and high precision.