Search Results (28)

Battery-powered electric unmanned surface vehicles (e-USVs) and electrified surface-cleaning platforms require reliable onboard vision under strict compute and power constraints. In reflective water environments, tiny floating debris is often obscured by specular highlights, reflection bands, ripples, motion blur, and camera jitter, while label noise further degrades training stability. To improve robustness without increasing onboard inference burden, this paper proposes YOLOv11-IMP, a lightweight detector for reflective water-surface scenes and embedded edge inference. The method integrates a transformer-enhanced backbone stage, a Global Channel–Spatial Attention module in the neck, and a median-enhanced channel–spatial module in the neck to improve global-context modeling, cross-scale interaction, and weak-boundary representation. WIoU-v3 is adopted to improve localization, and a train-time-only noise-aware screening strategy based on the small-loss principle is introduced to suppress unreliable labels without extra inference cost. Experiments on the CAS dataset and a self-built debris dataset show gains of 3.3% in AP@0.75 and 6.5% in AP for small objects over YOLOv11, while maintaining 7.3 GFLOPs and real-time inference on Jetson Nano, demonstrating practical potential for energy-constrained onboard missions. 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

Marine plastic pollution represents a critical ecological challenge, exerting long-lasting impacts on ecosystems, biodiversity, and human well-being. This study introduces the DEEP-PLAST project, an integrated AI-based framework designed for the detection and trajectory prediction of floating marine plastic waste using open-access Sentinel-2 satellite imagery and environmental models of ocean currents and wind. The DEEP-PLAST methodology integrates object detection (YOLOv5 on UAV data), semantic segmentation (U-Net/U-Net++ on Sentinel-2), and drift simulation using Copernicus and NOAA datasets. U-Net++ achieved the best performance (F1 = 0.84, false positive rate 5.2%), outperforming other models. Detected debris locations were linked to Lagrangian drift models to identify accumulation zones in the Black Sea, supporting targeted cleanup efforts. While promising, drift validation remains qualitative due to limited ground truth, to be addressed in future work with in situ and NGO data. This approach supports EU Mission Ocean, the Marine Strategy Framework Directive, and UN SDGs, demonstrating the potential of AI and remote sensing for marine protection. Future efforts will expand datasets, apply the platform to other seas, and launch a web tool for NGOs and policymakers. Full article

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters. Full article

Rapid rises in water levels due to heavy rainfall can lead to the accumulation of floating debris, posing significant challenges for both water quality and resource management. However, real-time monitoring of floating debris remains difficult due to the discrepancy between meteorological conditions and the timing of debris accumulation. To address this limitation, this study proposes an amplitude change detection (ACD) model based on time-series synthetic aperture radar (SAR) imagery, which is less affected by weather conditions. The model statistically distinguishes floating debris from open water based on their differing scattering characteristics. The ACD approach was applied to 18 pairs of Sentinel-1 SAR images acquired over Daecheong Reservoir from June to September 2024. A stringent type I error threshold (α < 1 × 10⁻⁸) was employed to ensure reliable detection. The results revealed a distinct cumulative effect, whereby the detected debris area increased immediately following rainfall events. A positive correlation was observed between 10-day cumulative precipitation and the debris-covered area. For instance, on 12 July, a floating debris area of 0.3828 km² was detected, which subsequently expanded to 0.4504 km² by 24 July. In contrast, on 22 August, when rainfall was negligible, no debris was detected (0 km²), indicating that precipitation was a key factor influencing the detection sensitivity. Comparative analysis with optical imagery further confirmed that floating debris tended to accumulate near artificial barriers and narrow channel regions. Overall, this study demonstrates that this spatial pattern suggests the potential to use detection results to estimate debris transport pathways and inform retrieval strategies. 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

Rivers act as natural conduits for the transport of plastic debris from terrestrial sources to marine environments. Accurately quantifying plastic debris in surface waters is essential for comprehensive environmental impact assessments. However, research on the detection of plastic debris in surface waters remains limited, particularly regarding real-time monitoring in natural environments following heavy rainfall events. This study aims to develop a real-time visual recognition model for floating plastic debris detection using deep learning with multi-class classification. A YOLOv8 algorithm was trained using field video data to automatically detect and count four types of floating plastic debris such as common plastics, plastic bottles, plastic film and vinyl, and fragmented plastics. Among the various YOLOv8 algorithms, YOLOv8-nano was selected to evaluate its practical applicability in real-time detection and portability. The results showed that the trained YOLOv8 model achieved an overall F1-score of 0.982 in the validation step and 0.980 in the testing step. Detection performance yielded mAP scores of 0.992 (IoU = 0.5) and 0.714 (IoU = 0.5:0.05:0.95). These findings demonstrate the model’s robust classification and detection capabilities, underscoring its potential for assessing plastic debris discharge and informing effective management strategies. Tracking and counting performance in an unknown video was limited, with only 6 of 32 observed debris items detected at the counting line. Improving tracking labels and refining data collection are recommended to enhance precision for applications in freshwater pollution monitoring. Full article

This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow. Full article

Ground-based detection of spaceborne dynamic objects, such as near-Earth asteroids and space debris, is essential for ensuring the safety of space operations. This paper presents YOLO-Dynamic, a novel detection algorithm aimed at addressing the limitations of existing models, particularly in complex environments and small-object detection. The proposed algorithm introduces two newly designed modules: the SC_Block_C2f and the LASF_Neck. SC_Block_C2f, developed in this study, integrates StarNet and Convolutional Gated Linear Unit (CGLU) operations, improving small-object recognition and feature extraction. Meanwhile, LASF_Neck employs a lightweight multi-scale architecture for optimized feature fusion and faster detection. The YOLO-Dynamic algorithm’s performance was validated on real-world images captured at Antarctic observatory sites. Compared to the baseline YOLOv8s model, YOLO-Dynamic achieved a 7% increase in mAP@0.5 and a 10.3% improvement in mAP@0.5:0.95. Additionally, the number of parameters was reduced by 1.48 M, and floating-point operations decreased by 3.8 G. These results confirm that YOLO-Dynamic not only delivers superior detection accuracy but also maintains computational efficiency, making it well suited for real-world applications requiring reliable and efficient spaceborne object detection. Full article

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution. Full article

The article describes the use of deep neural networks to detect small floating objects located in a vessel’s path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris, or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test the real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time. Full article

For the requirements of the multi-means observation and emergency monitoring of space objects, including space debris and near-earth asteroids, this paper analyzes the astronomical observation conditions in the stratosphere, which is the region of the earth’s atmosphere between 18 km and 55 km of altitude. The results reveal that near space has a significantly superior sky background and observation environment than ground-based observation, with the values of transmittance in the visible band and near-infrared bands more than 0.91 and 0.988, respectively. The sky background radiance at 20 km is 2.5% of the ground in the visible band and near-infrared band, which is practical for daytime observation, and there is an advantage in the availability of observable hours without the influence of aerosols and turbulence, etc. Based on near-space aerostats, such as a high-altitude balloon, a new method of space object floating observation has been proposed, including the observation facilities and scheme. The simulation shows that it has an all-weather/all-day ability while adopting multi-band observation. Applying a telescope with 9.5 mag detective ability located on the aerostat, debris with the size of about 0.36 m can be observed at a 1000 km distance and phase angle of 100°, while the near-earth asteroid with the size of about 980 km can be observed at a 5 million km distance and phase angle of 40° during the daytime. With these advantages, the aerostat-based observation would be a beneficial supplement to the ground-based observation network. Full article

Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the largest accumulation of marine litter that often goes undetected. Plastic debris makes up the majority of ocean litter and is a known source of microplastics in the ocean. This paper focuses on the detection of ocean plastic using neural network models. Two neural network models will be trained, i.e., YOLACT and the Mask R-CNN, for the instance segmentation of underwater litter in images. The models are trained on the TrashCAN dataset, using pre-trained model weights trained using COCO. The trained neural network could achieve a mean average precision ($mAP$) of 0.377 and 0.365 for the Mask R-CNN and YOLACT, respectively. The lightweight nature of YOLACT allows it to detect images at up to six times the speed of the Mask R-CNN, while only making a comparatively smaller trade-off in terms of performance. This allows for two separate applications: YOLACT for the collection of litter using autonomous underwater vehicles (AUVs) and the Mask R-CNN for surveying litter distribution. Full article

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination. Full article

Large-area, artificial floating marine litter (FML) targets were deployed during a controlled field experiment and data acquisition campaign: the Plastic Litter Project 2021. A set of 22 Sentinel-2 images, along with UAS data and ancillary measurements were acquired. Spectral analysis of the FML and natural debris (wooden planks) targets was performed, along with spectral comparison and separability analysis between FML and other floating materials such as marine mucilage and pollen. The effects of biofouling and submersion on the spectral signal of FML were also investigated under realistic field conditions. Detection of FML is performed through a partial unmixing methodology. Floating substances such as pollen exhibit similar spectral characteristics to FML, and are difficult to differentiate. Biofouling is shown to affect the magnitude and shape of the FML signal mainly in the RGB bands, with less significant effect on the infrared part of the spectrum. Submersion affects the FML signal throughout the range of the Sentinel-2 satellite, with the most significant effect in the NIR part of the spectrum. Sentinel-2 detection of FML can be successfully performed through a partial unmixing methodology for FML concentrations with abundance fractions of 20%, under reasonable conditions. Full article