Search Results (19)

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments remains a significant challenge, as targets are frequently obscured by high-frequency wave clutter, and feature distributions are destabilized by covariate shifts caused by illumination. To address these limitations, this study proposes YOLO11-MCN, a real-time detection framework that integrates two architectural components specifically designed for water-surface monitoring. The Multi-Scale Contextual Attention (MSCA) module distinguishes target signatures from background noise by aggregating contextual information across heterogeneous receptive fields, thereby suppressing false positives generated by waves. The Channel Normalization Attention Mechanism (CNAM) addresses illumination instability through feature statistic calibration based on Group Normalization, effectively mitigating covariate shifts induced by extreme lighting variations. Furthermore, these components are complemented by a high-resolution P2 detection head, which recovers the geometric details of small-scale targets typically lost during downsampling. Extensive experiments conducted on a dataset of 5812 images demonstrate that YOLO11-MCN achieves an mAP@0.5 of 92.7%, outperforming the YOLO11n baseline by 5.9 percentage points. Robustness evaluations confirm that MSCA and CNAM significantly reduce missed detections under severe wave clutter and backlighting conditions. With a recall of 90.5%, an inference speed of 94 FPS on desktop hardware, and a compact footprint of 3.89M parameters and 14.8 GFLOPs, the proposed framework offers a robust and efficient solution for intelligent water-surface surveillance systems within the single-class detection paradigm evaluated in this study, with strong potential for edge-device deployment following platform-specific optimization. Full article

Floating debris detection in complex aquatic environments holds significant importance for water resource protection and maritime safety monitoring. However, this task faces three core challenges: severe background interference leading to blurred target textures, significant non-rigid deformations, and the frequent loss of small targets at long distances. To address these issues, we propose a high-performance lightweight detection algorithm, termed High-Efficiency Edge-Aware Multi-Scale Real-Time Detection Transformer (HEMS-RTDETR), built upon the Real-Time Detection Transformer (RT-DETR) architecture. First, to suppress disturbances induced by water surface ripples and specular reflections, a Cross-Stage Partial Multi-Scale Edge Information Enhancement (CSP-MSEIE) module is introduced to reconstruct the backbone network. By removing computational redundancy while incorporating explicit edge enhancement, feature extraction capability and noise robustness for weak-texture targets are significantly improved. Second, to handle irregular debris morphology, a Deformable Attention Transformer (DAT) module is integrated, enabling adaptive attention focusing on geometrically deformed regions. Finally, an Efficient Multi-Scale Bidirectional Feature Pyramid Network (EMBSFPN) is constructed to enhance cross-scale semantic interaction and alleviate small-target signal loss. Experimental results demonstrate that, compared with RTDETR-r18, HEMS-RTDETR reduces parameters to 12.57 M, improves mAP@0.5 and mAP@0.5:0.95 by 2.44% and 3.05%, respectively, and maintains real-time inference at 93 FPS, indicating strong robustness and application potential in dynamic aquatic environments. Full article

Protecting aquatic biodiversity while ensuring reliable hydropower production and water supply remains a core challenge for both water security and biosecurity. In this PRISMA-based systematic review, we synthesize evidence from 96 studies on fish guidance and deterrence at hazardous water intakes. We examine non-physical barriers, including acoustic and light cues, electric fields, bubble curtains, and chemical stimuli, as well as physical barriers such as racks, guidance structures, and nets or screens that aim to divert fish away from intakes and toward selective passage routes. Overall, guidance and deterrence performance is strongly species- and site-specific. Multimodal systems that combine multiple cues show the highest mean guidance efficiency (~80%), followed by light-based deterrents (~77%). Acoustic, electric, and bubble barriers generally achieve intermediate efficiencies (~55–58%), whereas structural devices alone exhibit lower mean performance (~46%), with substantial variability among sites and designs. Physical screens remain effective for larger size classes but can increase head loss and debris accumulation. By contrast, non-physical systems offer more flexible, low-footprint options whose success depends critically on local hydraulics, the sensory ecology of target species, and ambient environmental conditions. We identify major knowledge gaps relating to underlying sensory and behavioral mechanisms, hydraulics-based design rules, and standardized performance metrics. We also highlight opportunities to integrate advanced monitoring and AI-based analytics into adaptive, site-specific guidance systems. Taken together, our findings show that carefully selected and tuned barrier technologies can provide practical pathways to enhance water security and biosecurity, while supporting sustainable fish passage, improving invasive-species control, and reducing ecological impacts at water infrastructure. Full article

This study evaluates the use of UAV-based photogrammetry to document shallow submerged cultural heritage, focusing on the Tirpitz wreck salvage site near Håkøya, Norway. Using a DJI Phantom 4 Multispectral drone, we acquired RGB and multispectral imagery over structures located at depths of up to 5–10 m. Structure-from-motion (SfM) processing enabled the three-dimensional reconstruction of submerged features, including a 52 × 10 m wharf and adjacent debris piles, with an accuracy of the order of 10 cm. Our data represents the first and only accurate mapping of the site yet carried out, with an absolute position uncertainty estimated to be no greater than 3 m. Volumes of imaged debris could be estimated, using a background subtraction method to allow for variable bathymetry, at around 350 m³. Bathymetric data for the sea floor could be derived effectively from an SfM point cloud, though less effectively applying the Stumpf model to the multispectral data as a result of significant spectral variation in the sea floor reflectance. Our results show that UAV-based through-surface SfM is a viable, low-cost method for reconstructing submerged heritage with high spatial accuracy. These findings support the integration of UAV-based remote sensing into heritage and environmental monitoring frameworks for shallow aquatic environments. Full article

Hurricanes are increasingly impacting inland water systems, yet their role in mobilizing anthropogenic contaminants remains insufficiently characterized. This study presents a preliminary assessment of organic contaminant loading in the French Broad River, North Carolina, 15 days after flooding from Hurricane Helene. Surface water samples from five sites were analyzed using liquid chromatography–high-resolution mass spectrometry. Targeted analysis quantified 11 per- and polyfluoroalkyl substances (PFASs), with summed concentrations ranging from 1.49 to 70.8 ng/L. One downstream site exhibited the highest burden, where PFOSs and PFOA exceeded U.S. EPA drinking water maximum contaminant levels. Non-targeted analysis (NTA) and suspect screening identified 468 compounds, with 96 structurally annotated at high confidence. Of these, a large proportion were associated with medium to high hazard potential, particularly for acute aquatic toxicity (69%), developmental toxicity (64%), mutagenic genotoxicity (49%), endocrine disruption (35%), skin irritation (27%), eye irritation (26%), and carcinogenicity (17%). Four EPA priority pollutants—4-nitrophenol, 2,4,6-trichlorophenol, pentachlorophenol, and dibutyl phthalate—were also detected. Site-specific chemical profiles suggested inputs from flood-damaged wastewater infrastructure and plastic debris. These findings highlight the susceptibility of freshwater systems to contaminant mobilization during extreme flooding and demonstrate the value of combining NTA and cheminformatics for post-disaster monitoring. Full article

Aquatic floating debris detection is a key technological foundation for ecological monitoring and integrated water environment management. It holds substantial scientific and practical value in applications such as pollution source tracing, floating debris control, and maritime navigation safety. However, this field faces ongoing challenges due to water surface polarization. Reflections of polarized light produce intense glare, resulting in localized overexposure, detail loss, and geometric distortion in captured images. These optical artifacts severely impair the performance of conventional detection algorithms, increasing both false positives and missed detections. To overcome these imaging challenges in complex aquatic environments, we propose a novel YOLOv8-based detection framework with integrated polarized light suppression mechanisms. The framework consists of four key components: a fisheye distortion correction module, a polarization feature processing layer, a customized residual network with Squeeze-and-Excitation (SE) attention, and a cascaded pipeline for super-resolution reconstruction and deblurring. Additionally, we developed the PSF-IMG dataset (Polarized Surface Floats), which includes common floating debris types such as plastic bottles, bags, and foam boards. Extensive experiments demonstrate the network’s robustness in suppressing polarization artifacts and enhancing feature stability under dynamic optical conditions. Full article

As global plastic production increases, the problem of marine plastic pollution is becoming increasingly critical, and the development of effective identification technologies is particularly urgent as plastic debris not only poses a threat to aquatic ecosystems but also has a significant impact on human health. This paper presents the criteria for evaluating fluorescence technology and its mechanism for plastic identification, with an emphasis on its potential for the rapid detection of marine plastic pollution. By analyzing variations in the fluorescence lifetimes and intensities of plastics, different types of plastics can be effectively distinguished. In addition, this paper reviews the detection of microplastics using different fluorescent dyes and explores the fluorescence lifetime identification method. This paper also demonstrates the effectiveness of fluorescence techniques for macroplastic identification, highlighting how fluorescence lifetimes and decay rates change in various weathering environments. Monitoring these changes offers a foundation for establishing weathering models, aiding in understanding the transformation of macrolitter into microplastics. Future research should investigate the autofluorescence properties of different plastics further and focus on developing detection methods and instruments for various environments. This will improve the identification of plastic waste in complex environments. In conclusion, fluorescence technology shows great promise in plastic identification and is expected to provide substantial support for recycling plastic waste products and mitigating plastic pollution. Full article

This study investigates underwater debris in a freshwater lake using remotely operated vehicles (ROVs) during two distinct survey periods: 2019 and 2024. The primary objective was to count and document visible debris (metal and plastic) on the lakebed based on ROV video recordings. A total of 356 debris items were observed in 2019, while only 39 items were recorded in 2024. The notable decrease in debris visibility in 2024 is likely attributed to dense algal growth during the survey months, which hindered the visual identification of objects on the lakebed. The study highlights the challenges of monitoring underwater debris in freshwater systems, particularly during periods of high algal activity, which can significantly impact visibility and detection efforts. While ROVs have proven effective in identifying submerged debris in clear water, this research underscores their limitations under reduced visibility conditions caused by algal blooms, turbidity diminishing the video quality. The results provide valuable insights into the temporal variation in debris visibility and contribute to ongoing efforts to improve freshwater debris monitoring techniques. Full article

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth. Full article

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies (mAP@0.5) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy (mAP@0.5) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14. Full article

Plastic debris is considered an emerging aquatic pollutant as an alarming number of reports are indicating the environmental contamination with such agents. Ichthyofauna has been subjected to increasing plastic pollution over the past years, which has led to detrimental effects in the food chain, and consequently to the general health of ecosystems. In this study, we exposed juvenile specimens of Oreochromis niloticus to polyvinyl chloride (PVC) in powder form. Specimens and water parameters were closely monitored for 40 days before tissue samples were collected for histological and biochemical analysis. Structural hepatic alterations were observed in specimens from the exposed groups, such as intercellular corridors, dilation of sinusoidal capillaries, hyperchromatic nuclei, nuclear hypertrophy, and cytoplasm vacuolization. Low catalase activity was observed in the case of 1000 mg of PVC/kg feed group, as well as high levels of malondialdehyde compared to the control group, indicating oxidative stress. Glutathione peroxidase activity was also significantly decreased in the 500 and 1000 mg/kg feed group compared to the control group. These findings suggest that a midterm exposure to PVC particles can significantly affect the activity of antioxidative enzymes in O. niloticus specimens and induce changes of hepatic tissue structure. Full article

Microplastics (the term for plastics at sizes of <5 mm) might be introduced into the environment from domestic or agricultural activities or from the breakdown of plastic pieces, particles, and debris that are bigger in size. Their presence in the aquatic environment has caused accumulation problems, as microplastics do not easily break down and can be digested by some aquatic organisms. This study was conducted to screen and monitor the level of microplastic pollution in polychaete worms using pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS). The study was conducted in Setiu Wetlands, Malaysia from November 2015 to January 2017 at five-month intervals and covered all monsoon changes. Results from physical and visual analyses indicated that a total number of 371.4 ± 20.2 items/g microplastics were retrieved from polychaete for all seasons, in which, the majority comprised transparent microplastics (49.87%), followed by brown with 138.3 ± 13.6 items/g (37.24%), 21.7 ± 1.9 items/g for blue (5.84%), and 12.9 ± 1.1 items/g for black (3.47%), while the remaining were green and grey-red colors. Statistical analysis using Kruskal–Wallis showed insignificant differences (p > 0.05) between the sampling station and period for the presence of a microplastics amount. Most of the microplastics were found in fiber form (81.5%), whereas the remaining comprised fragment (18.31%) and film (0.19%) forms. Further analysis with Py-GC/MS under a selective ion monitoring mode indicated that pyrolytic products and fragment ions for a variety of polymers, such as polyvinyl chloride, polypropylene, polyethylene, polyethylene terephthalate, polyamide, and polymethylmethacrylate, were detected. This study provides an insightful application of Py-GC/MS techniques for microplastics monitoring, especially when dealing with analytical amounts of samples. Full article

Taking into consideration the essential contribution of Mytilus galloprovincialis farming, it is of rising importance to add knowledge regarding bacterial species occurrence in water samples from aquaculture zones from the point of view of both the organism and public health. In the present study, we investigated the bacterial community existing in water samples from six Mytilus galloprovincialis aquaculture areas in the Thermaikos gulf, northern Greece, that may provoke toxicity in aquatic organisms and humans and may indicate environmental pollution in mussel production as well as algal blooms. Bacterial species were identified molecularly by sequencing of a partial 16s rRNA segment and were analyzed phylogenetically for the confirmation of the bacterial taxonomy. The results obtained revealed the presence of four bacterial genera (Halomonas sp., Planococcus sp., Sulfitobacter sp., and Synechocystis sp.). Members of the Halomonas and Sulfitobacter genera have been isolated from highly polluted sites, Planococcus bacteria have been identified in samples derived directly from plastic debris, and Synechocystis bacteria are in line with microcystin detection. In this context, the monitoring of the bacteria community in mussel aquaculture water samples from the Thermaikos gulf, the largest mussel cultivation area in Greece, represents an indicator of water pollution, microplastics presence, algal blooms, and toxin presence. Full article

Mangroves are areas that connect the land and sea, and are important to the ecosystem. They are important places for food sources and the habitat of aquatic fauna in the tidal areas. However, the existence of plastic debris poses a risk to the aquatic environment. This study aimed to investigate the accumulation of microplastics (MPs) in sediment cores from two mangrove areas. The first mangrove area is in the outer section of the Songkhla lagoon (SK), while the second is in the coastal area of Pattani province (PN). Sediment core sampling was performed from SK = 8 stations and PN = 5 stations. Surface enrichment of MP was observed, especially in sediments of 0–4 cm. MPs were found throughout the depth in both areas, while fewer MPs were found in deeper sediment core layers (p < 0.05) at some stations inside the mangrove zone. Simple linear regression of the observed MPs and distance in the horizontal were found to be significant at SK within the mangrove zone with r² = 0.79 (p < 0.05). MP fibers were the most commonly found MP type in both areas and were less than 1 mm. Blue and black MPs were the most abundant colors found in both areas. The six polymer types reported in this study comprised polyethylene, rayon, rubber, styrene, Poly (vinyl acetate), and paint. The findings of the present study suggest that long-term monitoring of marine debris along coastlines is necessary to help improve national policies and measures related to marine plastic debris. Full article

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable. Full article