Search Results (224)

To address the significant degradation of image visibility and contrast in turbid media, this paper proposes an enhanced image dehazing algorithm. Unlike traditional polarimetric dehazing methods that exclusively attribute polarization information to airlight, our approach integrates object radiance polarization and airlight polarization for haze removal. First, sky regions are localized through multi-scale fusion of polarization and intensity segmentation maps. Second, region-specific transmittance estimation is performed by differentiating haze-occluded regions from haze-free regions. Finally, target radiance is solved using boundary constraints derived from non-haze regions. Compared with other dehazing algorithms, the method proposed in this paper demonstrates greater adaptability across diverse scenarios. It achieves higher-quality restoration of targets with results that more closely resemble natural appearances, avoiding noticeable distortion. Not only does it deliver excellent dehazing performance for land fog scenes, but it also effectively handles maritime fog environments. Full article

Target detection is essential in intelligent transportation and autonomous control of unmanned aerial vehicles (UAVs), with single-stage detection algorithms used widely due to their speed. However, these algorithms face limitations in detecting small targets, especially in aerial photography from unmanned aerial vehicles (UAVs), where small targets are often occluded, multi-scale semantic information is easily lost, and there is a trade-off between real-time processing and computational resources. Existing algorithms struggle to effectively extract multi-dimensional features and deep semantic information from images and to balance detection accuracy with model complexity. To address these limitations, we developed FFG-YOLO, a lightweight small-target detection method for UAVs based on YOLOv8. FFG-YOLO incorporates three modules: a feature enhancement block (FEB), a feature concat block (FCB), and a global context awareness block (GCAB). These modules strengthen feature extraction from small targets, resolve semantic bias in multi-scale feature fusion, and help differentiate small targets from complex backgrounds. We also improved the positioning accuracy of small targets using the Wasserstein distance loss function. Experiments showed that FFG-YOLO outperformed other algorithms, including YOLOv8n, in small-target detection due to its lightweight nature, meeting the stringent real-time performance and deployment requirements of UAVs. Full article

Noncommunicable diseases (NCDs) like diabetes and cancer drive demand for therapeutic functional foods. This study developed freeze-dried fermented camel milk (FCM) with Ajwa date pulp (ADP), evaluating its physical and functional properties, probiotic survival, and potential benefits for diabetes and cancer. To achieve this target, six FCM formulations were prepared using ABT-5 starter culture (containing Lactobacillus acidophilus, Bifidobacterium bifidum, and Streptococcus thermophilus) with or without Lacticaseibacillus rhamnosus B-1937 and ADP (12% or 15%). The samples were freeze-dried, and their functional properties, such as water activity, dispersibility, water absorption capacity, water absorption index, water solubility index, insolubility index, and sedimentation, were assessed. Reconstitution properties such as density, flowability, air content, porosity, loose bulk density, packed bulk density, particle density, carrier index, Hausner ratio, porosity, and density were examined. In addition, color and probiotic survivability under simulated gastrointestinal conditions were analyzed. Also, antidiabetic potential was assessed via α-amylase and α-glucosidase inhibition assays, while cytotoxicity was evaluated using the MTT assay on Caco-2 cells. The results show that ADP supplementation significantly improved dispersibility (up to 72.73% in FCM15D+L). These improvements are attributed to changes in particle size distribution and increased carbohydrate and mineral content, which facilitate powder rehydration and reduce clumping. All FCM variants demonstrated low water activity (0.196–0.226), indicating good potential for shelf stability. The reconstitution properties revealed that FCM powders with ADP had higher bulk and packed densities but lower particle density and porosity than controls. Including ADP reduced interstitial air and increased occluded air within the powders, which may minimize oxidation risks and improve packaging efficiency. ADP incorporation resulted in a significant decrease in lightness (L*) and increases in redness (a*) and yellowness (b*), with greater pigment and phenolic content at higher ADP levels. These changes reflect the natural colorants and browning reactions associated with ADP, leading to a more intense and visually distinct product. Probiotic survivability was higher in ADP-fortified samples, with L. acidophilus and B. bifidum showing resilience in intestinal conditions. The FCM15D+L formulation exhibited potent antidiabetic effects, with IC₅₀ values of 111.43 μg mL⁻¹ for α-amylase and 77.21 μg mL⁻¹ for α-glucosidase activities, though lower than control FCM (8.37 and 10.74 μg mL⁻¹, respectively). Cytotoxicity against Caco-2 cells was most potent in non-ADP samples (IC₅₀: 82.22 μg mL⁻¹ for FCM), suggesting ADP and L. rhamnosus may reduce antiproliferative effects due to proteolytic activity. In conclusion, the study demonstrates that ADP-enriched FCM is a promising functional food with enhanced probiotic viability, antidiabetic potential, and desirable physical properties. This work highlights the potential of camel milk and date synergies in combating some NCDs in vitro, suggesting potential for functional food application. Full article

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring. Full article

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare, progressive form of pre-capillary pulmonary hypertension characterized by persistent, organized thromboemboli in the pulmonary vasculature, leading to vascular remodeling, elevated pulmonary artery pressures, right heart failure, and significant morbidity and mortality if untreated. Despite advances, CTEPH remains underdiagnosed due to nonspecific symptoms and overlapping features with other forms of pulmonary hypertension. Basic Methodology: This review synthesizes data from large international registries, epidemiologic studies, translational research, and multicenter clinical trials. Key methodologies include analysis of registry data to assess incidence and risk factors, histopathological examination of lung specimens, and molecular studies investigating endothelial dysfunction and inflammatory pathways. Diagnostic modalities and treatment outcomes are evaluated through observational studies and randomized controlled trials. Recent Advances and Affected Population: Research has elucidated that CTEPH arises from incomplete resolution of pulmonary emboli, with subsequent fibrotic transformation mediated by dysregulated TGF-β/TGFBI signaling, endothelial dysfunction, and chronic inflammation. Affected populations are typically older adults, often with prior venous thromboembolism, splenectomy, or prothrombotic conditions, though up to 25% have no history of acute PE. The disease burden is substantial, with delayed diagnosis contributing to worse outcomes and higher societal costs. Microvascular arteriopathy and PAH-like lesions in non-occluded vessels further complicate the clinical picture. Conclusions: CTEPH is now recognized as a treatable disease, with multimodal therapies—surgical endarterectomy, balloon pulmonary angioplasty, and targeted pharmacotherapy—significantly improving survival and quality of life. Ongoing research into molecular mechanisms and biomarker-driven diagnostics promises earlier identification and more personalized management. Multidisciplinary care and continued translational investigation are essential to further reduce mortality and optimize outcomes for this complex patient population. Full article

To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is embedded into the SPPF module to construct an SPPF-LSKA fusion module, enhancing multi-scale feature representation for peach targets. Second, an MPDIoU-based bounding box regression loss function replaces CIoU to improve localization accuracy for overlapping and occluded peaches. The DyHead Block is integrated into the detection head to form a DMDetect module, strengthening feature discrimination for small and occluded targets in complex backgrounds. To address insufficient feature fusion flexibility caused by scale variations from occlusion and illumination differences in multi-scale peach detection, a novel Adaptive Multi-Scale Fusion Pyramid (AMFP) module is proposed to enhance the neck network, improving flexibility in processing complex features. Experimental results demonstrate that SDA-YOLO achieves precision (P), recall (R), mAP@0.95, and mAP@0.5:0.95 of 90.8%, 85.4%, 90%, and 62.7%, respectively, surpassing YOLOv11n by 2.7%, 4.8%, 2.7%, and 7.2%. This verifies the method’s robustness in complex orchard environments and provides effective technical support for intelligent fruit harvesting and yield estimation. Full article

Background/Objectives: Accurately predicting the arrival position of a moving target is essential in sports and daily life. While predictive saccades are known to enhance performance, the neural mechanisms underlying the timing of these strategies remain unclear. This study investigated how the timing of saccadic strategies—executed early versus late—affects cortical activity patterns, as measured by electroencephalography (EEG). Methods: Sixteen participants performed a task requiring them to predict the arrival position and timing of a parabolically moving target that became occluded midway through its trajectory. Based on eye movement behavior, participants were classified into an Early Saccade Strategy Group (SSG) or a Late SSG. EEG signals were analyzed in the low beta band (13–15 Hz) using the Hilbert transform. Group differences in eye movements and EEG activity were statistically assessed. Results: No significant group differences were observed in final position or response timing errors. However, time-series analysis showed that the Early SSG achieved earlier and more accurate eye positioning. EEG results revealed greater low beta activity in the Early SSG at electrode sites FC6 and P8, corresponding to the frontal eye field (FEF) and middle temporal (MT) visual area, respectively. Conclusions: Early execution of predictive saccades was associated with enhanced cortical activity in visuomotor and motion-sensitive regions. These findings suggest that early engagement of saccadic strategies supports more efficient visuospatial processing, with potential applications in dynamic physical tasks and digitally mediated performance domains such as eSports. Full article

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field conditions due to their small size, severe overlapping and occlusion, and the large parameter sizes of existing models. To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. By incorporating additional learnable parameters, the PGCD enhances the detection and localization of small targets. Finally, channel pruning based on the Layer-Adaptive Magnitude-based Pruning (LAMP) score is applied to reduce model parameters and runtime memory. Experimental results on the Rapeseed Flower-Raceme Benchmark (RFRB) demonstrate that the SPL-YOLOv8n-prune model achieves a detection accuracy of 92.2% in Average Precision (AP₅₀), comparable to SOTA methods, while reducing the giga floating point operations per second (GFLOPs) and parameters by 86.4% and 95.4%, respectively. The model size is only 0.5 MB and the real-time frame rate is 171 fps. The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding. Full article

Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework that enables robust Sim-to-Real adaptation. Specifically, we first develop a dual-branch strategy for self-supervised feature learning, integrating Masked Autoencoders and contrastive learning. This approach extracts domain-invariant representations from unlabeled simulated imagery to enhance robustness against occlusion while reducing annotation dependency. Leveraging these learned features, we then introduce a 3D Transformer fusion module that unifies multi-view RGB and LiDAR point clouds through cross-modal attention. By explicitly modeling spatial layouts and height differentials, this component significantly improves recognition of small and occluded targets in complex low-altitude environments. To address persistent fine-grained domain shifts, we finally design region-level adversarial calibration that deploys local discriminators on partitioned feature maps. This mechanism directly aligns texture, shadow, and illumination discrepancies which challenge conventional global alignment methods. Extensive experiments on UAV benchmarks VisDrone and DOTA demonstrate the effectiveness of RA3T. The framework achieves +5.1% mAP on VisDrone and +7.4% mAP on DOTA over the 2D adversarial baseline, particularly on small objects and sparse occlusions, while maintaining real-time performance of 17 FPS at 1024 × 1024 resolution on an RTX 4080 GPU. Visual analysis confirms that the synergistic integration of 3D geometric encoding and local adversarial alignment effectively mitigates domain gaps caused by uneven illumination and perspective variations, establishing an efficient pathway for simulation-to-reality UAV perception. Full article

To address challenges of background interference and limited multi-scale feature extraction in infrared small target detection, this paper proposes a YOLO-HVS detection algorithm inspired by the human visual system. Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch depth-separable convolution to suppress background noise and enhance occluded targets, integrating local details and global context. Meanwhile, the C2f_DWR (dilation-wise residual) module with regional-semantic dual residual structure is designed to significantly improve the efficiency of capturing multi-scale contextual information by expanding convolution and two-step feature extraction mechanism. We construct the DroneRoadVehicles dataset containing 1028 infrared images captured at 70–300 m, covering complex occlusion and multi-scale targets. Experiments show that YOLO-HVS achieves mAP50 of 83.4% and 97.8% on the public dataset DroneVehicle and the self-built dataset, respectively, which is an improvement of 1.1% and 0.7% over the baseline YOLOv8, and the number of model parameters only increases by 2.3 M, and the increase of GFLOPs is controlled at 0.1 G. The experimental results demonstrate that the proposed approach exhibits enhanced robustness in detecting targets under severe occlusion and low SNR conditions, while enabling efficient real-time infrared small target detection. Full article

Ensuring that electrical workers use personal protective equipment (PPE) correctly is critical to electrical safety, but existing detection methods face significant limitations when applied in the electrical industry. This paper introduces MRC-DETR (Multi-Scale Re-calibration Detection Transformer), a novel framework for detecting Power Engineering Personal Protective Equipment (PEPPE) in complex electrical operating environments. Our method introduces two technical innovations: a Multi-Scale Enhanced Boundary Attention (MEBA) module, which significantly improves the detection of small and occluded targets through optimized feature representation, and a knowledge distillation strategy that enables efficient deployment on edge devices. We further contribute a dedicated PEPPE dataset to address the lack of domain-specific training data. Experimental results demonstrate superior performance compared to existing methods, particularly in challenging power industry scenarios. Full article

Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. Concurrently, deep learning-based pose control algorithms suffer from inherent training instability, slow convergence rates, and inefficient learning processes in complex environments characterized by high-density fruit clusters and occluded picking scenarios. To address these challenges, this paper proposes an enhanced reinforcement learning framework DRFW-TQC that integrates Dynamic L2 Regularization for adaptive model stabilization and a Group-Wise Feature Weighting Network for discriminative feature representation. The methodology further incorporates a picking posture traction mechanism to optimize end-effector orientation control. The experimental results demonstrate the superior performance of DRFW-TQC compared to the baseline. The proposed approach achieves a 16.0% higher picking success rate and a 20.3% reduction in angular error with four target strawberries. Most notably, the framework’s transfer strategy effectively addresses the efficiency challenge in complex environments, maintaining an 89.1% success rate in eight-strawberry while reducing the timeout count by 60.2% compared to non-adaptive methods. These results confirm that DRFW-TQC successfully resolves the tripartite challenge of operational precision, training stability, and environmental adaptability in robotic fruit harvesting systems. Full article

The accurate classification of cocoa pod ripeness is critical for optimizing harvest timing, improving post-harvest processing, and ensuring consistent quality in chocolate production. Traditional ripeness assessment methods are often subjective, labor-intensive, or destructive, highlighting the need for automated, non-invasive solutions. This study evaluates the performance of R-CNN-based deep learning models—Faster R-CNN and Mask R-CNN—for the detection and segmentation of cocoa pods across four ripening stages (0–2 months, 2–4 months, 4–6 months, and >6 months) using the RipSetCocoaCNCH12 dataset, which is publicly accessible, comprising 4116 labeled images collected under real-world field conditions, in the context of precision agriculture. Initial experiments using pretrained weights and standard configurations on a custom COCO-format dataset yielded promising baseline results. Faster R-CNN achieved a mean average precision (mAP) of 64.15%, while Mask R-CNN reached 60.81%, with the highest per-class precision in mature pods (C4) but weaker detection in early stages (C1). To improve model robustness, the dataset was subsequently augmented and balanced, followed by targeted hyperparameter optimization for both architectures. The refined models were then benchmarked against state-of-the-art YOLOv8 networks (YOLOv8x and YOLOv8l-seg). Results showed that YOLOv8x achieved the highest mAP of 86.36%, outperforming YOLOv8l-seg (83.85%), Mask R-CNN (73.20%), and Faster R-CNN (67.75%) in overall detection accuracy. However, the R-CNN models offered valuable instance-level segmentation insights, particularly in complex backgrounds. Furthermore, a qualitative evaluation using confidence heatmaps and error analysis revealed that R-CNN architectures occasionally missed small or partially occluded pods. These findings highlight the complementary strengths of region-based and real-time detectors in precision agriculture and emphasize the need for class-specific enhancements and interpretability tools in real-world deployments. Full article

The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. This study proposes SWMD-YOLO, a lightweight multi-scale detection network optimized for greenhouse scenarios. The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. Traditional down-sampling is replaced with a dynamic sample (DySample) operator to minimize information loss during resolution transitions, while a multi-scale convolutional attention (MSCA) mechanism prioritizes discriminative regions under varying illumination. Additionally, we introduce Focaler-IoU, a novel loss function that addresses sample imbalance by dynamically re-weighting gradients for partially occluded and multi-scale targets. Experiments on greenhouse tomato data sets demonstrate that SWMD-YOLO achieves 93.47% mAP50 with a detection speed of 75.68 FPS, outperforming baseline models in accuracy while reducing parameters by 18.9%. Cross-data set validation confirms the model’s robustness to complex backgrounds and lighting variations. Overall, the proposed model provides a computationally efficient solution for real-time crop monitoring in resource-constrained precision agriculture systems. Full article

Due to the inherent limitations of visible-light sensors, monitoring systems that rely solely on single-modal visible-light images exhibit reduced accuracy, posing safety concerns in applications such as autonomous driving. Infrared and visible-light image fusion technology addresses this issue by generating composite images that integrate complementary information from both modalities, thereby enhancing perception robustness. This study focuses on target detection in fused images. Given that targets in such images are often small and severely occluded, we propose an optimized detection framework to overcome these challenges. Specifically, we improve the YOLOv8 baseline model by introducing a dedicated small-object detection layer, incorporating the Global Attention Mechanism (GAM), and refining the loss function. Experimental results show that our method achieves a 5.0% improvement in mAP and a 6.5% gain in recall over the original YOLOv8. Furthermore, comparative experiments on fused and single-modal inputs demonstrate that fused images yield the highest detection accuracy. These results confirm that leveraging fused inputs significantly enhances detection accuracy and robustness in complex environments. Full article