Search results

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

The purpose of this paper was to develop a novel capacitive micromachined ultrasonic transducer (CMUT) reception and transmission linear array for underwater imaging at 400 kHz. Compared with traditional CMUTs, the developed transducer array offers higher electromechanical coupling coefficient and higher directivity performance.

The configuration of the newly developed CMUT reception and transmission array was determined by the authors’ previous research into new element structures with patterned top electrodes and into directivity simulation analysis. Using the Si-Silicon on insulator (Si-SOI) bonding technique and the principle of acoustic impedance matching, the CMUT array was fabricated and packaged. In addition, underwater imaging system design and testing based on the packaged CMUT 1 × 16 array were completed.

The simulation results showed that the optimized CMUT array configuration was selected. Furthermore, the designed configuration of the CMUT 1 × 16 linear array was good enough to guarantee high angular resolution. The underwater experiments were conducted to demonstrate that this CMUT array can be of great benefit in imaging applications.

Based on our research, the CMUT linear array has good directivity and good impedance matching with water and can be used for obstacle avoidance, distance measurement and imaging underwater.

This research provides a basis for CMUT directivity theory and array design. CMUT array presented in this paper has good directivity and has been applied in the underwater imaging, resulting in a huge market potential in underwater detection systems.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

Laser imaging and ranging systems offer 3‐D imaging of video quality at distances four to five times greater than conventional video and photographic systems. The Spotmap system is based on the laser radar approach, using a pulsed green solid state laser as a transmitter and detecting the time‐of‐flight and peak intensity of the reflected pulses. Images and maps of underwater scenes are obtained by scanning the laser beam across the objects in a raster pattern using a two‐axis optical scanner. The system can be used both in linescan and 2D scan modes of operation such that images can be obtained either from the linear movement of the remotely operated vehicle or from a stationary position. The system has a 90∞ cross‐track FOV and is capable of processing 8,000 points per second, which gives an imaging resolution comparable to video at ROV speeds up to 2m/s.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition.

This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic.

A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers.

The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information.

This study aims to simulate and test the performance of a transmitting and receiving capacitive micro-machined ultrasonic transducer (CMUT). Aimed at detecting demand of the CMUT, a matched integrated adjustment circuit was designed through analyzing processing methods of transducer’s weak echo signal.

Based on the analysis of CMUT array structure and work principle, the CMUT units are designed and the dynamic performance analysis of SIMULINK is given according to the demand of underwater detecting. A transceiver isolation circuit is used to make transmission mode and receiving mode separate. A detection circuit is designed based on the transimpedance amplifier to achieve extraction of high-frequency and weak signal.

Through experimentation, the effectiveness of the CMUT performance simulation and the transceiver integrated adjustment circuit were verified. In addition, the test showed that CMUT with 400 kHz frequency has wider bandwidth and better dynamic characteristics than other similar transducers.

This paper provides a theoretical basis and design reference for the development and application of CMUT technology.

The purpose of this paper is to illustrate the growing role of robots in environmental monitoring.

Following an introduction, this first considers aerial robots for monitoring atmospheric pollution. It then discusses the role of aerial, surface and underwater robots to monitor aquatic environments. Some examples are then provided of the robotic monitoring of the terrestrial environment, and finally, brief conclusions are drawn.

Robots are playing an important role in numerous environmental monitoring applications and have overcome many of the limitations of traditional methodologies. They operate in all media and frequently provide data with enhanced spatial and temporal coverage. In addition to detecting pollution and characterising environmental conditions, they can assist in locating illicit activities. Drones have benefited from the availability of small and lightweight imaging devices and sensors that can detect airborne pollutants and also characterise certain features of aquatic and terrestrial environments. As with other robotic applications, environmental drone imagery is benefiting from the use of AI techniques. Ranging from short-term local deployments to extended-duration oceanic missions, aquatic robots are increasingly being used to monitor and characterise freshwater and marine environments.

This provides a detailed insight into the growing number of ways that robots are being used to monitor the environment.

The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.

The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.

As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms.

This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification.

The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand.

There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs.

The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles.