Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.

With the increasing application and study of lightweight and high strength fiber reinforced polymer composites in ocean industry, the structural failure problem of composite pressure hulls has attracted great attention from many researchers in China and globally. Analysis of the structural failure mechanisms is foundational to the design of deep-sea composite pressure hulls, since nowadays the design rules of pressurized vessels is mostly formulated according to their failure modes. Hence, this paper aims to review the research on the structural failure of composite pressure hulls in deep sea settings. First of all, the applied research status on composite material in marine equipment is analyzed, including inspection modalities for composite pressure hulls. The review then focuses on the three main failure modes, namely overall buckling, material failure and snap buckling of the deep-sea composite pressure hulls. The study identifies further problems of composite pressure hulls to be solved through the application of the deep sea equipment research, aiming to provide a reference for the study of mechanical behavior, ultimate strength computation, and design of thick composite pressure hulls for deep sea equipment.

Biomimetic aquatic robots are a promising solution for marine applications such as internal pipe inspection, beach safety, and animal observation because of their strong manoeuvrability and low environmental damage. As the application field of robots has changed from a structured known environment to an unstructured and unknown territory, the disadvantage of the low efficiency of the propeller propulsion has become more crucial. Among the various actuation methods of biomimetic robots, many researchers have utilised fluid actuation as fluid is clean, environmentally friendly, and easy to obtain. This paper presents a literature review of the locomotion mode, actuation method, and typical works on fluid-driven bionic aquatic robots. The actuator and structural material selection is then discussed, followed by research direction and application prospects of fluid-driven bionic aquatic robots.

The detection of metal particles in oil is of great significance to the fault diagnosis of mechanical equipment. Real domain detection methods cannot simultaneously identify the sizes and materials of particles. Because a metal particle in an alternating magnetic field induces a complex domain signal whose strength relates to the particle properties, this paper introduces a double lock-in amplifier circuit (DLAC) into a triple-coil inductive particle sensor to detect the complex domain signal under noise interference conditions. Furthermore, various sizes of red copper, brass, aluminum, iron, and type 403 steel particles are chosen as the experimental materials. For the first time, the complex domain signals induced by the five kinds of particles are detected. Hence, in the complex plane, a method is proposed to identify the size and material of a particle based on the amplitude and phase of these signals. With the high sensitivity and strong antinoise ability of the DLAC, this system can detect 25 μm iron particles and 70 μm copper particles. This study presents a new strategy for the online detection of particles in oil.

In the field of underwater vision, image matching between the main two sensors (sonar and optical camera) has always been a challenging problem. The independent imaging mechanism of the two determines the modalities of the image, and the local features of the images under various modalities are significantly different, which makes the general matching method based on the optical image invalid. In order to make full use of underwater acoustic and optical images, and promote the development of multisensor information fusion (MSIF) technology, this letter proposes to apply an image attribute transfer algorithm and advanced local feature descriptor to solve the problem of underwater acousto-optic image matching. We utilize real and simulated underwater images for testing; experimental results show that our proposed method could effectively preprocess these multimodal images to obtain an accurate matching result, thus providing a new solution for the underwater multisensor image matching task.

Image feature matching is essential in many computer vision applications, and the foundation of matching is feature detection, which is a crucial feature quantification process. This manuscript focused on detecting more features from underwater acoustic imageries for further ocean engineering applications of autonomous underwater vehicles (AUVs). Currently, the mainstream feature detection operators are developed for optical images, and there is not yet a feature detector oriented to underwater acoustic imagery. To better analyze the suitability of existing feature detectors for acoustic imagery and develop an operator that can robustly detect feature points in underwater imageries in the future, this manuscript compared the performance of well-established handcrafted feature detectors and that of the increasingly popular deep-learning-based detectors to fill the gap in the literature. The datasets tested are from the most commonly used side-scan sonars (SSSs) and forward-looking sonars (FLSs). Additionally, the detection idea of these detectors on the acoustic imagery phase congruency (PC) layer was innovatively proposed with the aim of finding a solution that balances detection accuracy and speed. The experimental results show that the ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) detectors achieve the best overall performance, the FAST detector is the fastest, and the PC and Sobel layers are the most favorable for implementing feature detection.