Analyse of Accuracy for Passive Estimation of Target Depth in Deep Sea Based on Vertical Linear Array

Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods.

A recent publication by McCargar and Zurk [(2013). J. Acoust. Soc. Am. 133(4), EL320-EL325] introduced a modified Fourier transform-based method for passive source depth estimation using vertical line arrays deployed below the critical depth in the deep ocean. This method utilizes the depth-dependent modulation caused by the interference between the direct and surface-reflected acoustic arrivals, the observation of which is enhanced by propagation through the reliable acoustic path. However, neither the performance of this method nor its limits of applicability have yet been thoroughly investigated. This paper addresses both of these issues; the first by identifying and analyzing the factors that influence the resolution and ambiguity in the transform-based depth estimate; the second by introducing another, much simpler depth estimation method, which is used to determine the target trajectories required for observation of the interference pattern and the array requirements for accurate depth estimation.

The scale of vertical linear array (VLA) used in deep-sea is usually several thousand meters. The accurate estimation of its array is an important prerequisite for the application of VLA signal processing technology. In this paper, a deep-sea vertical array measurement method using depth sensor and azimuth sensor is proposed. Firstly, a number of depth sensors and azimuth sensors set on different depth nodes are used to obtain the tilt angle and tilt azimuth angle of deep-sea vertical array at different depth nodes. Then, through polynomial curve fitting method, the inclination angle and azimuth angle of deep-sea vertical array at different depths are recursively processed. Finally, the three-dimensional coordinate position of the deep-sea VLA is calculated, and the three-dimensional formation of the deep-sea vertical array is obtained. The experimental data processing results show that the VLA shape in a 4000-meter sea area of South China Sea may be controlled by the northeast-southwest sea current and changes at any time in a 12-hour tidal cycle, and the relative variation distance of the top of the VLA can reach 500 m at most.

A system based on vertical linear array is brought forward. The localization model is presented, and the localization principle of vertical linear array is discussed. With range, elevation angle induced from time-delay and the measurement of azimuth angle, three dimensional equation of state and equation of measurement have been established. The localization precise according to length of baseline and precise of time-delay is simulated and analyzed. According to the observability principle of nonlinear system, the observability qualifications of the system are given.

Monocular depth estimation is a fundamental yet challenging task in computer vision as depth information will be lost when 3D scenes are mapped to 2D images. Although deep learning-based methods have led to considerable improvements for this task in a single image, most existing approaches still fail to overcome this limitation. Supervised learning methods model depth estimation as a regression problem and, as a result, require large amounts of ground truth depth data for training in actual scenarios. Unsupervised learning methods treat depth estimation as the synthesis of a new disparity map, which means that rectified stereo image pairs need to be used as the training dataset. Aiming to solve such problem, we present an encoder-decoder based framework, which infers depth maps from monocular video snippets in an unsupervised manner. First, we design an unsupervised learning scheme for the monocular depth estimation task based on the basic principles of structure from motion (SfM) and it only uses adjacent video clips rather than paired training data as supervision. Second, our method predicts two confidence masks to improve the robustness of the depth estimation model to avoid the occlusion problem. Finally, we leverage the largest scale and minimum depth loss instead of the multiscale and average loss to improve the accuracy of depth estimation. The experimental results on the benchmark KITTI dataset for depth estimation show that our method outperforms competing unsupervised methods.

The benefits of binocular vision have been debated throughout the history of vision science yet few studies have considered its contribution beyond a viewing distance of a few meters. In the first set of experiments, we compared monocular and binocular performance on depth interval estimation and discrimination tasks at 4.5, 9.0 or 18.0 m. Under monocular conditions, perceived depth was significantly compressed. Binocular depth estimates were much nearer to veridical although also compressed. Regression-based precision measures were much more precise for binocular compared to monocular conditions (ratios between 2.1 and 48). We confirm that stereopsis supports reliable depth discriminations beyond typical laboratory distances. Furthermore, binocular vision can significantly improve both the accuracy and precision of depth estimation to at least 18 m. In another experiment, we used a novel paradigm that allowed the presentation of real binocular disparity stimuli in the presence of rich environmental cues to distance but not interstimulus depth. We found that the presence of environmental cues to distance greatly enhanced stereoscopic depth constancy at distances of 4.5 and 9.0 m. We conclude that stereopsis is an effective cue for depth discrimination and estimation for distances beyond those traditionally assumed. In normal environments, distance information from other sources such as perspective can be effective in scaling depth from disparity.

Depth estimation is an essential component in understanding the 3D geometry of a scene. In comparison to traditional depth estimation methods such as structure from motion and stereo vision matching, determining depth relation using a single camera is challenging. The recent advancements in convolutional neural networks have accelerated the research in monocular depth estimation. However, most technologies infer depth maps using lower resolution images due to network capacity and complexity issues. Another challenge in depth estimation is ambiguous and sparse depth maps. These issues are caused due to labeling errors, hardware faults, or occlusions. This paper presents a novel end-to-end trainable convolutional neural network architecture – depth transverse transformer network (DTTNet). The proposed network is designed and optimized to perform monocular depth estimation. This network aims at exploring the multi-resolution representation to perform pixel-wise depth estimation more accurately. In order to improve the accuracy of depth estimation, different kinds of ad hoc networks are proposed subsequently. Extensive computer simulations on NYU Depth V2 and SUN RGB-D dataset demonstrate the effectiveness of the proposed DTTNet against state-of-the-art methods. DTTNet can potentially optimize depth perception in intelligent systems such as automated driving and video surveillance applications, computational photography, and augmented reality. The source code is available at https://github.com/shreyaskamathkm/DTTNet

Monocular endoscopy images depth estimation with multi-scale residual fusion

Estimation of modal wavenumbers is important for inference of geoacoustic properties and data-driven matched field processing in shallow water waveguides. This paper introduces a deep neural network called combining physical informed neural network (CPINN) for modal wavenumber estimation using a vertical linear array (VLA). Note that the sound field recorded by a VLA can be expressed as a linear superposition of finite modal depth functions, and the differential equations satisfied by the modal depth functions are related to the corresponding modal wavenumbers. CPINN can estimate the modal wavenumbers by introducing the proxies of the modal depth functions and constraining them to satisfy the corresponding differential equations. The performance of the CPINN is evaluated by simulated data in a noisy shallow water environment. Numerical results show that, when compared with the previous methods, CPINN does not need to know the exact horizontal distance between the sound source and the VLA. Moreover, CPINN can estimate the modal wavenumbers at the VLA position in the case where the range segment traversed by the source, i.e., the aperture in the range direction, is smaller than the maximum modal cycle distance and in a range-dependent waveguide.

Underground mapping is of paramount importance at archaeological sites with natural or man‐made caves. Techniques of different complexity are available for underground surveying, from compass and tape to light detection and ranging (LiDAR). However, there are scenarios where it is impossible to use heavy and/or delicate instrumentation, or with some of the more advanced techniques, long‐distance and time‐consuming fieldwork would be required. This is the case of the study of the height of water at key points inside the aquifer located in the subsoil of the Roman city of Colonia Clunia Sulpicia to assess its relation with the evolution of the city. Although subsurface radiolocation, a technique originally intended for cave mapping, seems promising for this application, its accuracy in depth estimation is not sufficient for this purpose. Therefore, the main objectives of this work are to improve the accuracy of depth estimation using radiolocation, to obtain sound data to study changes in water supply in the early centuries of the city and to hypothesize possible causes and probable consequences. Then, this work analyses the sources of error affecting the radiolocation process and develops a new method experimentally validated to improve the depth estimation accuracy. As a result, the depth of key points at Clunia has been accurately measured by radiolocation, where LiDAR or direct measurements from the water level are not possible. Finally, based on the resultant data, a chronology of the city in relation to the aquifer, and vice versa, is outlined. The new radiolocation and calculation procedure proposed in this paper is an improved subsurface location technique that can lead to a significant innovation in archaeological prospection.

Source depth estimation is always a problem in underwater acoustic area, because depth estimation is a nonlinear problem. Traditional depth estimation methods use a vertical line array, which has disadvantage of poor mobility due to the size of sensor array. In order to estimate source depth with a horizontal line array, we propose a matched-mode depth estimation method based on autoregressive (AR) wavenumber estimation for a moving source in shallow water waveguides. First, we estimate the mode wavenumbers using the improved AR modal wavenumber spectrum. Second, according to the mode wavenumber estimation, we estimate the mode amplitudes by the wavenumber spectrum, which is obtained by generalized Hankel transform. Finally, we estimate source depth estimation by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with synthetic aperture beamforming, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The robustness of the proposed method is illustrated by simulating the performance in mismatched environment.

Deep neural networks have been applied to a wide range of problems in recent years. Convolutional neural network is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "semiparallel deep neural network (SPDNN)" eliminates duplicated common network layers and can be further optimized by retraining to achieve an improved model compared to the individual topologies. Four SPDNN models are trained and have been evaluated at two stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using postprocessing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study. (C) 2018 SPIE and IS&T

This paper presents a new algorithm for enforcing temporal coherence on depth estimation from multi-view videos of dynamic scenes as well as the first substantial quantitative evaluation of the improvement in depth estimation accuracy due to temporal coherence. The proposed algorithm is generally applicable and practical since it bypasses explicit scene flow estimation, which has a very large state space, and relies only on optical flow which is used to impose soft constraints on depth estimation for the next frame. As a result, our algorithm is applicable to scenes with large depth and motion ranges. The output is a sequence of depth maps that can be used for novel view synthesis among other applications. While it is intuitive that enforcing temporal coherence should improve the accuracy of depth estimation, this improvement has never been assessed quantitatively due to the lack of data with ground truth. To overcome this limitation we use the image prediction error as the criterion and show that the benefits of temporal coherence are significant on a diverse set of multi-view video sequences.

Depth estimation from 2D images is an essential task in computer vision with applications in scene understanding, robotics, and autonomous systems. The performance of supervised depth models depends on network design, loss formulation, data quality, and fine-tuning strategy. In this study, we propose a progressive fine-tuning approach for metric (absolute-scale) depth estimation. Our method uses transfer learning across multiple indoor datasets: real, synthetic, and pseudo-labelled. DenseNet-169 and EfficientNet-B0 backbones are fine-tuned on MIT-G, SUN-RGBD, SceneNet, and NYU2. We apply a three-scale combined loss with weighted MAE + Edge + SSIM terms at full, 1/2, and 1/4 resolution, and add a perceptual VGG component, while we keep the global coefficients of the loss at 1 for simplicity and reproducibility. We find that EfficientNet performs better on the smaller datasets, while DenseNet benefits most from the million-image SceneNet stage and reaches REL 0.105 and RMSE 0.359 on NYU2, comparable to recent transformer baselines yet using 6 $$\times $$ × fewer parameters. The pseudo-labelled MIT-G data is used as a warm-start and shows the potential of reducing annotation cost. All headline metrics results are based on sensor ground-truth data, avoiding circular evaluation. Qualitative analysis and zero-shot tests on the unseen iBims-1 benchmark confirm that the models generalise and produce coherent, detailed depth maps across diverse indoor scenes. The proposed pipeline thus offers a balanced trade-off between accuracy and computational cost for practical indoor depth estimation.

In deep water, multipath time delays or frequency-domain interference periods of the acoustic intensity combined with multipath arrival angles are typically used for source localization. However, depth estimate is hard to achieve for a narrowband source at a remote part of the direct arrival zone as the required bandwidth increases with the source range. In this paper, a passive source localization method with a vertical line array, suitable for both broadband and narrowband sources, is proposed. Based on the variation trends of multipath angles with source range and depth, source localization is achieved by only matching the measured angles of the direct path and surface-reflected path with model-based values of a predefined grid of potential source locations. Considering the angle resolution limited by the array aperture and the presence of coherent multipath, sparse Bayesian learning is used and compared with the conventional beamforming and the minimum-variance distortionless-response beamforming to resolve and estimate the multipath angles. Simulations and experimental data of explosive sources collected by a vertical line array in the South China Sea are carried out to illustrate the method and demonstrate the performance.