Target detection using features for sonar images

Tueller, Peter; Kastner, Ryan; Diamant, Roee

doi:10.1049/iet-rsn.2020.0224

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…When receiving signals, 4 to 8 sonar-receiving transducers around the transmitting probe simultaneously convert the received acoustic signals into electrical signals, and the signals are transmitted to the microcomputer through the sonar signal processing module [14]. e sonar signal processing module is composed of digital circuits, completing the preprocessing of the received signal.…”

Section: Pile Bottom Karst Cave Sonar Detector Jl-sonarmentioning

confidence: 99%

Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment

Liu¹,

Wang²,

Guo³

et al. 2022

Journal of Control Science and Engineering

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In order to solve the problem that the current geophysical method is difficult to locate in the mud of bored piles, the problem of effective exploration of karst caves at the bottom of piles, the author puts forward the application of Internet of Things technology and sonar technology in the construction of bored pile foundation in a high altitude environment. Combined with the propagation characteristics of sonar stress waves, a sonar detection method for karst caves at the bottom of bored piles is proposed, and a sonar detector for karst caves at the bottom of piles (JL-SONAR) and signal analysis software (PBCA) are developed. The JL-SONAR detector realizes the emission and acquisition of onsite sonar signals, and the PBCA software completes the analysis and arrangement of the detection data. This technology makes full use of the mud conditions of bored piles, the development of karst caves within 10 m of the pile bottom can be tracked and detected in the process of hole formation, which has the advantages of low cost, high speed, and high precision. Experimental results show that Project A has been drilled in multiple directions at the bottom of the pile, the rock cores on the west side of the pile bottom are mostly fragmented, the rock quality index RQD is 15–30, and the quality of the foundation rock at the pile bottom is extremely poor, which verify the results of sonar detection. No slurry leakage occurred in Project B after drilling, and the cores were mostly short columns. The rock quality index RQD was 75 to 90, and the quality of the foundation rock at the bottom of the pile was good, which verifies the results of sonar detection. Conclusion. The research results provide a new solution for karst cave exploration in karst areas, especially in liquid environments.

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Section: Pile Bottom Karst Cave Sonar Detector Jl-sonarmentioning

confidence: 99%

Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment

Liu¹,

Wang²,

Guo³

et al. 2022

Journal of Control Science and Engineering

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“…e proposed framework first estimates the seabed type according to the spatial distribution of the features to determine the best parameter set and then obtains a set of features, which are filtered according to the intensity and distribution to produce detection decisions. e proposed method also provides a method to determine the type of seabed and a method based on machine learning to select the parameters of the feature detector to match the type of seabed evaluated, but the accuracy of the matching evaluation result is not particularly high [6]. Lubis et al proposed an image denoising method based on two-dimensional wavelet transform and applied it to the seabed recognition data acquisition system.…”

Section: Related Workmentioning

confidence: 99%

Sonar Image Target Detection Based on Deep Learning

2022

Mathematical Problems in Engineering

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Due to the nonlinearity of the imaging of sonar equipment and the complexity of the underwater sound field environment, the gray level of the target area of the acquired underwater sonar image is relatively small. These characteristics are the target of the subsequent sonar image. Work such as detection and location tracking has brought great challenges. It has brought great challenges to solving the work of positioning and tracking, which makes the research of sonar image target detection based on deep learning very important. This article aims at studying the use of sonar to detect image targets based on deep learning technology. This article proposes a variety of sound image denoising methods based on multiresolution tools. The purpose of this article is to divide the natural image into blocks at an appropriate rate according to the change of the sampling matrix and measure the underwater natural image. The sound image defines an information model. These methods have greatly changed the image and period of using remote and temporary information. The translation results of these methods are all valid. The sharpening separation method based on filtered image and bidirectional detection should be published through a solution algorithm and different frames, and the expected algorithm can be reused and extracted as an action to improve the similarity of the image and should be saved and separated in detail. The result is correct. This article studies the application of deep learning methods in sonar image target detection and designs corresponding algorithms for improvement and functional realization in view of the current deficiencies and needs in this field. The experimental results show that the improved scheme and applied algorithm proposed in this article can achieve good results, the verification sample set includes 184 remote-sensing aircraft targets, and the resolution of remote-sensing images is unified to 1644 × 971 size. The accuracy of the target detection algorithm has been significantly improved, reaching 74.6%, and the detection speed has also been greatly improved. Compared with the RNN algorithm, the speed has been increased by 7 times. The detection results confirmed that the improved algorithm has higher positioning accuracy and faster detection speed.

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“…However, based on the current state of the art, it is more challenging to extract acoustic features of the reflector from the received echo, even though bats appear to be able to deal with the challenge. As another example, a recent study uses a feature extraction algorithm for object detection in a sonar image [48]. In addition, there are general techniques for data association such as the use of Mahalanobis distance [34] and joint probabilistic data association [49].…”

Section: Simultaneous Localization and Mapping Using Extended Kalman ...mentioning

confidence: 99%

Eavesdropping like a bat: Towards fusing active and passive sonar for a case study in simultaneous localization and mapping

Shirazi

Abaid

2021

IET Radar Sonar & Navi

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Among so-called active sensors that use self-generated signals, sonar sensors are more challenging to implement than lidar and radar due in part to their limited angular field of sensing. A common solution to this challenge is scanning sensors that sweep an angular range with successive measurements. However, scanning sensors are particularly problematic for sonar because of the relatively slow sound speed and the inertia of the sonar head. Studies of bat behaviour suggest that bats may eavesdrop on their conspecifics during group flight. In other words, they fuse information gathered by their own active sonar with information they receive by passively listening to peers. Because bats are extremely skilled in using sonar, this behaviour inspired an investigation into whether fusing active and passive sonar can be a solution to the challenges of implementing sonar sensors. A model of fused sensing is defined, and a numerical simulation is used to answer this question on the test bed problem of simultaneous localization and mapping (SLAM). The simulation results show that when the angular range of active sonar and associated noise is relatively small, the robot's performance in solving SLAM is improved.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Target detection using features for sonar images

Cited by 20 publications

References 22 publications

Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment

Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment

Sonar Image Target Detection Based on Deep Learning

Eavesdropping like a bat: Towards fusing active and passive sonar for a case study in simultaneous localization and mapping

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