Search Results (313)

The placement of hydraulic elements in existing pool-type fishways to make them more suitable for Cyprinid fish is an issue of increasing interest in fishway research. Hydrodynamic characteristics and fish behavior at the representative pool of the fishway with bottom orifices and notches were assessed at the Dagdelen hydropower plant in the Ceyhan River Basin, Türkiye. Three-dimensional velocity measurements were taken in the pool of the fishway using an Acoustic Doppler velocimeter. The measurements were taken with and without a brush block at two different vertical distances from the bottom, which were below and above the level of bristles tips. A computational fluid dynamics (CFD) analysis was conducted for the studied fishway. The numerical model utilized Large Eddy Simulation (LES) combined with the Darcy–Forchheimer law, wherein brush blocks were represented as homogenous porous media. Our results revealed that the relative submergence of bristles in the brush block plays a very important role in velocity and Reynolds shear stress (RSS) distributions. After the placement of the submerged brush block, flow velocity and the lateral RSS component were reduced, and a resting area was created behind the brush block below the bristles’ tips. Fish movements in the pool were recorded by underwater cameras under real-time operation conditions. The heatmap analysis, which is a 2-dimensional fish spatial presence visualization technique for a specific time period, showed that Capoeta damascina avoided the areas with high turbulent fluctuations during the tests, and 61.5% of the fish presence intensity was found to be in the low Reynolds shear regions in the pool. This provides a clear case for the real-world ecological benefits of retrofitting existing pool-weir fishways with such flexible hydraulic elements. Full article

Accurate evaluation of fish feeding intensity is crucial for optimizing aquaculture efficiency and the healthy growth of fish. Previous methods mainly rely on single-modal approaches (e.g., audio or visual). However, the complex underwater environment makes single-modal monitoring methods face significant challenges: visual systems are severely affected by water turbidity, lighting conditions, and fish occlusion, while acoustic systems suffer from background noise. Although existing studies have attempted to combine acoustic and visual information, most adopt simple feature-level fusion strategies, which fail to fully explore the complementary advantages of the two modalities under different environmental conditions and lack dynamic evaluation mechanisms for modal reliability. To address these problems, we propose the Adaptive Cross-modal Attention Fusion Network (ACAF-Net), a cross-modal complementarity learning framework with a two-stage attention fusion mechanism: (1) a cross-modal enhancement stage that enriches individual representations through Low-rank Bilinear Pooling and learnable fusion weights; (2) an adaptive attention fusion stage that dynamically weights acoustic and visual features based on complementarity and environmental reliability. Our framework incorporates dimension alignment strategies and attention mechanisms to capture temporal–spatial complementarity between acoustic feeding signals and visual behavioral patterns. Extensive experiments demonstrate superior performance compared to single-modal and conventional fusion approaches, with 6.4% accuracy improvement. The results validate the effectiveness of exploiting cross-modal complementarity for underwater behavioral analysis and establish a foundation for intelligent aquaculture monitoring systems. Full article

Recent advances in passive acoustic monitoring (PAM) have markedly improved the ability to study marine soundscapes by enabling long-term, non-invasive monitoring of biological sounds across large spatial and temporal scales. Among aquatic organisms, fish are primary contributors to biophony, producing sounds associated with feeding, reproduction, and social behavior. However, the majority of previous research has focused on individual vocalizations, with limited attention to collective acoustic phenomena such as fish choruses. This study quantitatively analyzes choruses produced by the small yellow croaker (Larimichthys polyactis), an ecologically and commercially important species in the Northwest Pacific Ocean. Using power spectral density (PSD) analysis, we examined long-term underwater recordings from a sea cage containing approximately 2000 adult small yellow croakers. The choruses were centered around ~600 Hz and exhibited sound pressure levels 15–20 dB higher at night than during the day. These findings highlight the ecological relevance of fish choruses and support their potential use as indicators of biological activity. This study lays the foundation for incorporating fish choruses into soundscape-based PAM frameworks to enhance biodiversity and habitat monitoring. Full article

In order to solve problems such as recognition of blind areas which exist in traditional technology in underwater near-field target sensing, this paper constructs an underwater robot target sensing model based on the fish lateral line sensing mechanism and adopts CFD simulation technology to analyze the perturbation characteristic law of the pressure signal in the flow field around the underwater robot. By extracting the pressure signal following the bionic lateral line on the surface of the underwater robot as the target recognition information, the SVM multi-target recognition model is trained and built to realize the perception and recognition of the structural features and attitude features of the underwater robot. The results show that the structural features and attitude features of the underwater robot can be recognized by using the time-domain waveform structural features and spatially symmetric distribution features of the pressure coefficients, and the recognition accuracy can reach over 90%, which reveals the principle of target feature resolution based on the sideline perception signals of the fish nerve center. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

The integration of biomimetic pectoral fins into robotic fish presents a promising approach to enhancing maneuverability, stability, and braking efficiency in underwater robotics. This study investigates a 1-DOF (degree of freedom) pectoral fin mechanism integrated into the SpineWave robotic fish. Through force measurements and particle image velocimetry (PIV), we optimized control parameters to improve braking and turning performances. The results show a 50% reduction in stopping distance, significantly enhancing agility and control. The fin-assisted braking and turning modes enable precise movements, making this approach valuable for autonomous underwater vehicles. This research lays the groundwork for adaptive fin designs and real-time control strategies, with applications in underwater exploration, environmental monitoring, and search-and-rescue operations. Full article

Precise identification and quantification of fish movement states are of significant importance for conducting fish behavior research and guiding aquaculture production, with object tracking serving as a key technical approach for achieving behavioral quantification. The traditional DeepSORT algorithm has been widely applied to object tracking tasks; however, in practical aquaculture environments, high-density cultured fish exhibit visual characteristics such as similar textural features and frequent occlusions, leading to high misidentification rates and frequent ID switching during the tracking process. This study proposes an underwater fish object tracking method based on the improved DeepSORT algorithm, utilizing ResNet as the backbone network, embedding Deformable Convolutional Networks v2 to enhance adaptive receptive field capabilities, introducing Triplet Loss function to improve discrimination ability among similar fish, and integrating Convolutional Block Attention Module to enhance key feature learning. Finally, by combining the aforementioned improvement modules, the ReID feature extraction network was redesigned and optimized. Experimental results demonstrate that the improved algorithm significantly enhances tracking performance under frequent occlusion conditions, with the MOTA metric improving from 64.26% to 66.93% and the IDF1 metric improving from 53.73% to 63.70% compared to the baseline algorithm, providing more reliable technical support for underwater fish behavior analysis. Full article

Underwater sounds play a key role in biodiversity as many marine animals use these to know their environment and to communicate among themselves. Unfortunately, anthropogenic noise makes this communication more difficult due to masking effects and may also produce harmful effects that compromise their preservation and survival. Many researchers have studied the influence of underwater noise on marine species in laboratory conditions using fish tanks. Consequently, studying the acoustic response of these fish tanks constitutes an essential task to better understand the results obtained in those experiments. In this work, a theoretical model and acoustic measurements were used to assess the uncertainty of a fish tank setup. The proposed methodology aims to improve the effectiveness of those studies involving fish tanks by an in-depth analysis of the sound field spatial distribution. Preliminary results show that this distribution depends on the frequency of the generated sound, the water level, and the measurement depth thus confirming the importance of analyzing the range of applicability of these setups. Full article

In recent years, China has been promoting aquaculture, but extensive water pollution caused by production activities and climate changes has resulted in losses exceeding 4.6 × 10⁷ kg of aquatic products. Widespread water pollution from production activities is a key issue that needs to be addressed in the aquaculture industry. Therefore, dynamic monitoring of water quality and fish-specific solutions are critical to the growth of fry. Here, a low-cost, small, and real-time monitorable bionic robotic fish based on YOLO-PWSL (PConv, Wise-ShapeIoU, and LGFB) is proposed to achieve intelligent control of aquaculture. The bionic robotic fish incorporates a caudal fin for propulsion and adaptive buoyancy control for precise depth regulation. It is equipped with various types of sensors and wireless transmission equipment, which enables managers to monitor water parameters in real time. It is also equipped with YOLO-PWSL, an improved underwater fish identification model based on YOLOv5s. YOLO-PWSL integrates three key enhancements. In fact, we designed a multilevel attention fusion block (LGFB) that enhances perception in complex scenarios, to optimize the accuracy of the detected frames, the Wise-ShapeIoU loss function was used, and in order to reduce the parameters and FLOPs of the model, a lightweight convolution method called PConv was introduced. The experimental results show that it exhibits excellent performance compared with the original model: the mAP@0.5 (mean average precision at the 0.5 IoU threshold) of the improved model reached 96.1%, the number of parameters and the amount of calculation were reduced by 1.8 M and 3.1 G, respectively, and the detected leakage was effectively reduced. In the future, the system will facilitate the monitoring of water quality and fish species and their behavior, thereby improving the efficiency of aquaculture. Full article

Compared with real fish, bionic fish have significant gaps in terms of swimming speed and efficiency, turning performance, and agility. The complicated underwater working environment necessitates monitoring equipment that can deal with the dynamic interference of dense fish schools and aquatic vegetation. An agile and flexible bionic fish with a fast swimming speed would be better suited to underwater monitoring tasks. In this study, a bionic greenfin fish robot is designed in detail, and a hydrodynamic simulation analysis of the designed bionic greenfin fish robot is carried out using STAR CCM+ and Fluent software to analyze the effects of different parameters on the propulsion performance of the pectoral fins, the steering of the caudal fins, and the emergency stop function. The swimming efficiency was found to be highest when the angle of attack was changed sinusoidally by 10° and the frequency was the same as that of the pectoral fin flutter. The feasibility of an emergency stop of the tail fin with negative-phase swinging and the adjustment of the pectoral fin uneven flutter monitoring position were also confirmed. Full article

Biodiversity monitoring requires the discovery of multi-target tracking. The main requirement is not to reduce the localization error but the continuity of the tracks: a high ratio between the duration of the track and the lifetime of the target. To this end, we present an algorithm for detecting and tracking mobile underwater targets that utilizes reflections from active acoustic emission of broadband signals received by a rigid hydrophone array. The method overcomes the problem of a high false alarm rate by applying a tracking approach to the sequence of received reflections. A 2D time–distance matrix is created for the reflections received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns that correspond to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single target. The position and velocity are estimated using the debiased converted measurement Kalman filter. The results are analyzed for simulated scenarios and for experiments in the Adriatic Sea, where six Global Positioning System (GPS)-tagged gilt-head seabream fish were released and tracked by a dedicated autonomous float system. Compared to four recent benchmark methods, the results show favorable tracking continuity and accuracy that is robust to the choice of detection threshold. Full article

Efficient deep learning models are crucial in resource-constrained environments, especially for marine image classification in underwater monitoring and biodiversity assessment. This paper presents LatentResNet, a computationally lightweight deep learning model involving two key innovations: (i) using the encoder from the proposed LiteAE, a lightweight autoencoder for image reconstruction, as input to the model to reduce the spatial dimension of the data and (ii) integrating a DeepResNet architecture with lightweight feature extraction components to refine encoder-extracted features. LiteAE demonstrated high-quality image reconstruction within a single training epoch. LatentResNet variants (large, medium, and small) are evaluated on ImageNet-1K to assess their efficiency against state-of-the-art models and on Fish4Knowledge for domain-specific performance. On ImageNet-1K, the large variant achieves 66.3% top-1 accuracy (1.7M parameters, 0.2 GFLOPs). The medium and small variants reach 60.8% (1M, 0.1 GFLOPs) and 54.8% (0.7M, 0.06 GFLOPs), respectively. After fine-tuning on Fish4Knowledge, the large, medium, and small variants achieve 99.7%, 99.8%, and 99.7%, respectively, outperforming the classification metrics of benchmark models trained on the same dataset, with up to 97.4% and 92.8% reductions in parameters and FLOPs, respectively. The results demonstrate LatentResNet’s effectiveness as a lightweight solution for real-world marine applications, offering accurate and lightweight underwater vision. Full article

Accurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity switches. To address these challenges, this paper proposes an improved fish counting framework integrating YOLOv8-DT for detection and Byte-OCSORT for tracking. YOLOv8-DT incorporates the Deformable Large Kernel Attention Cross Stage Partial (DLKA CSP) module for adaptive receptive field adjustment and the Triple Detail Feature Infusion (TDFI) module for enhanced multi-scale feature fusion, improving small-object detection and occlusion robustness. Byte-OCSORT extends OC-SORT by integrating ByteTrack’s two-stage matching and a Class-Aware Cost Matrix (CCM), reducing ID switches and improving multi-species tracking stability. Experimental results on real-world underwater datasets demonstrate that YOLOv8-DT achieves a mAP₅₀ of 0.971 and mAP_50:95 of 0.742, while Byte-OCSORT reaches a MOTA of 72.3 and IDF1 of 69.4, significantly outperforming existing methods, confirming the effectiveness of the proposed framework for robust and accurate fish counting in complex aquatic environments. Full article

The marine environment is highly complex, characterized by substantial fluctuations in flow velocity. To enhance the adaptability of robotic large yellow croakers to such conditions, this study takes into account multiple factors, including shape, dimensions, and material properties, and evaluates their hydrodynamic resistance characteristics. A 2D model of large yellow croakers aged 1, 4, 7, 10, and 12 months was established as the bionic object. Based on computational fluid dynamics, the water resistance characteristics of this model were investigated in the same water environment. A 3D model of this species based on the 2D model and three skin materials, PE, PC, and ST, was added, and the effects of these materials on the water resistance of the 3D model were investigated. It was shown that in a water environment with a current speed of 0.1~1 m/s, the water resistance of large yellow croaker models at different ages ranged from 0.1006 to 6.8485 N; that of croakers with different body lengths ranged from 0.1067 to 28.5760 N; and that of croakers with different skin materials ranged from 0.0048 to 0.8672 N. The results showed that in the water environment with a current speed of 0.1–1 m/s, the 12-month-old large yellow croaker model had a lower water resistance range of 0.1006~3.6512 N in the watershed compared with other models of the same age; the large yellow croaker models with body lengths of 20, 30, and 40 cm had a smaller range of water resistance of 0.1125~12.5110 N in the watershed compared with other models of the same body length; and large yellow croaker models made of PE had a smaller range of resistance of 0.0048~0.7523 N in the watershed compared to those made of PC and ST materials. The results of this study are important for the design and fabrication of robotic fish capable of prolonged underwater operations. Full article

The reef fish communities of the Guanahacabibes National Park have been studied for 20 years using various methodologies that have allowed us to understand aspects of their diversity and structure. However, due to gaps in information about the abundance and distribution of mesopredators (big fish and sharks), a new study was conducted in 2017 to determine their structure, explore the influence of different factors on their spatial variability, and evaluate their behavior. To achieve this, the Baited Remote Underwater Video Surveys (BRUVs) methodology was successfully applied, locating a single set of BRUVs at 90 sites distributed across 9 sectors of the park’s functional zoning. Variability in mesopredator metrics and their potential prey was assessed through a PERMANOVA analysis; a distance-based linear model (DISTLM) was used to explore the relationship between mesopredator abundance and biological, abiotic, and condition variables; and animal behavior was classified as incidental, cautious, or aggressive. A total of 64 fish species were identified, 7 of which were mesopredators, and 3 were sharks. An uneven distribution and abundance were observed among sectors, with the most abundant mesopredators being Carcharhinus perezi, Sphyraena barracuda, and Mycteroperca bonaci. Mesopredator abundance was more closely related to the condition of zone use and its regulations than to biological and abiotic variables. Sharks were more abundant in strictly protected areas, which coincided with relatively murky waters and stronger currents. More than 50% of the observed sharks displayed exploratory and aggressive behavior towards the bait basket. The analyzed metrics validate the effectiveness of the management of the protected area and suggest the presence of healthy and resilient mesopredator fish communities. Full article