Search Results (1,356)

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a multi-scale attention module, and optimizes the C3k2 structure to improve the detection and precise localization of small targets. Secondly, a streamlined GSConv convolutional module is incorporated to minimize the parameter count and computational load while effectively retaining inter-channel dependencies. Finally, a novel and efficient cross-scale connected neck network is designed to achieve information complementarity and feature fusion among different scales, efficiently capturing multi-scale semantics while decreasing the complexity of the model. In contrast with the baseline model, the method proposed in this paper demonstrates notable benefits for use in underwater devices constrained by limited computational capabilities. The results demonstrate that AGS-YOLO significantly outperforms previous methods in terms of accuracy on the DUO underwater dataset, with mAP@0.5 improving by 1.3% and mAP@0.5:0.95 improving by 2.6% relative to those of the baseline YOLO11n model. In addition, the proposed model also shows excellent performance on the RUOD dataset, demonstrating its competent detection accuracy and reliable generalization. This study proposes innovative approaches and methodologies for underwater small-target detection, which have significant practical relevance. Full article

Polyomaviruses are a family of small DNA viruses capable of establishing persistent infections, and they can pose significant pathogenic risks in immunocompromised hosts. While traditionally studied in the context of viral reactivation and immune suppression, recent evidence has highlighted the gut microbiota as a critical regulator of host immunity and viral pathogenesis. This review examines the complex interactions between polyomaviruses, the immune system, and intestinal microbiota, emphasizing the role of short-chain fatty acids (SCFAs) in modulating antiviral responses. We explore how dysbiosis may facilitate viral replication, reactivation, and immune escape and also consider how polyomavirus infection can, in turn, alter microbial composition. Particular attention is given to the Firmicutes/Bacteroidetes ratio as a potential biomarker of infection risk and immune status. Therapeutic strategies targeting the microbiota, including prebiotics, probiotics, and fecal microbiota transplantation (FMT), are discussed as innovative adjuncts to immune-based therapies. Understanding these tri-directional interactions may offer new avenues for mitigating disease severity and improving patient outcomes during viral reactivation. Full article

Aluminothermic reduction is an alternative processing route for the circular economy because Al is produced electrochemically in the Hall–Héroult process with minimal CO₂ emissions if the electricity input is sourced from non-fossil fuel energy sources. This circular processing option attracts increased research attention in the aluminothermic production of manganese and silicon alloys. The Al₂O₃ product must be recycled through hydrometallurgical processing, with leaching as the first step. Recent work has shown that the NaAlO₂ compound is easily leached in water. In this work, a suitable slag formulation is applied in the aluminothermic reduction of manganese ore to form a Na₂O-based slag of high Al₂O₃ solubility to effect good alloy–slag separation. The synergistic effect of carbon and silicon reductants with aluminium is illustrated and compared to the test result with only carbon reductant. The addition of small amounts of carbon reductant to MnO₂-containing ore ensures rapid pre-reduction to MnO, facilitating aluminothermic reduction. At 1350 °C, a loosely sintered mass formed when carbon was added alone. The alloy and slag chemical analyses are compared to the thermochemistry predicted phase chemistry. The alloy consists of 66% Mn, 22–28% Fe, 2–9% Si, 0.4–1.4% Al, and 2.2–3.5% C. The higher %Si alloy is formed by adding Si metal. Although the product slag has a higher Al₂O₃ content (52–55% Al₂O₃) compared to the target slag (39% Al₂O₃), the fluidity of the slags appears sufficient for good alloy separation. Full article

The application of image recognition technology plays a vital role in agricultural disease identification. Existing approaches primarily rely on image classification, object detection, or semantic segmentation. However, a major challenge in current semantic segmentation methods lies in accurately identifying small target objects. In this study, common tobacco leaf diseases—such as frog-eye disease, climate spots, and wildfire disease—are characterized by small lesion areas, with an average target size of only 32 pixels. This poses significant challenges for existing techniques to achieve precise segmentation. To address this issue, we propose integrating two attention mechanisms, namely cross-feature map attention and dual-branch attention, which are incorporated into the semantic segmentation network to enhance performance on small lesion segmentation. Moreover, considering the lack of publicly available datasets for tobacco leaf disease segmentation, we constructed a training dataset via image splicing. Extensive experiments were conducted on baseline segmentation models, including UNet, DeepLab, and HRNet. Experimental results demonstrate that the proposed method improves the mean Intersection over Union (mIoU) by 4.75% on the constructed dataset, with only a 15.07% increase in computational cost. These results validate the effectiveness of our novel attention-based strategy in the specific context of tobacco leaf disease segmentation. Full article

Weed infestation contributes significantly to global agricultural yield loss and increases the reliance on herbicides, raising both economic and environmental concerns. Effective weed detection in agriculture requires high accuracy and architectural efficiency. This is particularly important under challenging field conditions, including densely clustered targets, small weed instances, and low visual contrast between vegetation and soil. In this study, we propose GTDR-YOLOv12, an improved object detection framework based on YOLOv12, tailored for real-time weed identification in complex agricultural environments. The model is evaluated on the publicly available Weeds Detection dataset, which contains a wide range of weed species and challenging visual scenarios. To achieve better accuracy and efficiency, GTDR-YOLOv12 introduces several targeted structural enhancements. The backbone incorporates GDR-Conv, which integrates Ghost convolution and Dynamic ReLU (DyReLU) to improve early-stage feature representation while reducing redundancy. The GTDR-C3 module combines GDR-Conv with Task-Dependent Attention Mechanisms (TDAMs), allowing the network to adaptively refine spatial features critical for accurate weed identification and localization. In addition, the Lookahead optimizer is employed during training to improve convergence efficiency and reduce computational overhead, thereby contributing to the model’s lightweight design. GTDR-YOLOv12 outperforms several representative detectors, including YOLOv7, YOLOv9, YOLOv10, YOLOv11, YOLOv12, ATSS, RTMDet and Double-Head. Compared with YOLOv12, GTDR-YOLOv12 achieves notable improvements across multiple evaluation metrics. Precision increases from 85.0% to 88.0%, recall from 79.7% to 83.9%, and F1-score from 82.3% to 85.9%. In terms of detection accuracy, mAP:0.5 improves from 87.0% to 90.0%, while mAP:0.5:0.95 rises from 58.0% to 63.8%. Furthermore, the model reduces computational complexity. GFLOPs drop from 5.8 to 4.8, and the number of parameters is reduced from 2.51 M to 2.23 M. These reductions reflect a more efficient network design that not only lowers model complexity but also enhances detection performance. With a throughput of 58 FPS on the NVIDIA Jetson AGX Xavier, GTDR-YOLOv12 proves both resource-efficient and deployable for practical, real-time weeding tasks in agricultural settings. 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

Foreign Object Debris (FOD) detection is paramount for airport operations. The precise identification and removal of FOD are critical for ensuring airplane flight safety. This study collected FOD images using optical imaging sensors installed at Urumqi Airport and created a custom FOD dataset based on these images. To address the challenges of small targets and complex backgrounds in the dataset, this paper proposes optimizations and improvements based on the advanced detection network Dab-Deformable. First, this paper introduces a Lightweight Deep-Shallow Feature Fusion algorithm (LDSFF), which integrates a hotspot sensing network and a spatial mapping enhancer aimed at focusing the model on significant regions. Second, we devise a Multi-Directional Deformable Channel Attention (MDDCA) module for rational feature weight allocation. Furthermore, a feedback mechanism is incorporated into the encoder structure, enhancing the model’s capacity to capture complex dependencies within sequential data. Additionally, when combined with a Threshold Selection (TS) algorithm, the model effectively mitigates the distraction caused by the serialization of multi-layer feature maps in the Transformer architecture. Experimental results on the optical small FOD dataset show that the proposed network achieves a robust performance and improved accuracy in FOD detection. Full article

Background/Objectives: Magnetic resonance imaging (MRI) is a powerful, non-invasive diagnostic tool capable of capturing detailed anatomical and physiological information. MRI contrast agents enhance image contrast but, especially linear gadolinium-based compounds, have been associated with safety concerns. This has prompted interest in alternative contrast agents. Manganese-based contrast agents offer a promising substitute, owing to manganese’s favorable magnetic properties, natural biological role, and strong T1 relaxivity. This review aims to critically assess the structure, mechanisms, applications, and challenges of manganese-based contrast agents in MRI. Methods: This review synthesizes findings from preclinical and clinical studies involving various types of manganese-based contrast agents, including small-molecule chelates, nanoparticles, theranostic platforms, responsive agents, and controlled-release systems. Special attention is given to pharmacokinetics, biodistribution, and safety evaluations. Results: Mn-based agents demonstrate promising imaging capabilities, with some achieving relaxivity values comparable to gadolinium compounds. Targeted uptake mechanisms, such as hepatocyte-specific transport via organic anion-transporting polypeptides, allow for enhanced tissue contrast. However, concerns remain regarding the in vivo release of free Mn²⁺ ions, which could lead to toxicity. Preliminary toxicity assessments report low cytotoxicity, but further comprehensive long-term safety studies should be carried out. Conclusions: Manganese-based contrast agents present a potential alternative to gadolinium-based MRI agents pending further validation. Despite promising imaging performance and biocompatibility, further investigation into stability and safety is essential. Additional research is needed to facilitate the clinical translation of these agents. Full article

Small-scale farms deserve attention and support because they play crucial and important roles. Apart from ensuring provision of food security, they also provide other economic, environmental, and social–cultural benefits. In the United States of America, these farms are agriculturally, culturally, and geographically different. They have varied needs that trigger an array of distinct biophysical, socioeconomic, and institutional challenges. The effects of these challenges are exacerbated by economic uncertainty, technological advancements, climate change, and other environmental concerns. To provide ideal services to the small-scale farm audience, it is necessary to understand these challenges and opportunities that can be leveraged to enhance their productivity and profitability. This article reviews the challenges faced by small-scale farming in the United States of America. It then reviews possible pathways to enhance their productivity and profitability. The review revealed that U.S. small-scale farms face several challenges. They include accessing farmland, credit and capital, lack of knowledge and skills, and technology adoption. Others are difficulties to insure, competition from corporations, and environmental uncertainties associated with climate change. The paper then reviews key pathways to enhance small-scale farmers’ capacities and resilience with a positive impact on their productivity and profitability. They are enhanced cooperative extension services, incentivization, strategic marketing, annexing technology, and government support, among others. Based on the diversity of farms and their needs, responses should be targeted towards individual needs. Since small-scale farm products have an effect on human health and dietary patterns, strategies to increase productivity should be linked to nutrition and health. Full article

Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting. Full article

Detecting small objects using remote sensing technology has consistently posed challenges. To address this issue, a novel detection framework named ACLC-Detection has been introduced. Building upon the Yolov11 architecture, this detector integrates an attention mechanism with lightweight convolution to enhance performance. Specifically, the deep and shallow convolutional layers of the backbone network are both introduced to depthwise separable convolution. Moreover, the designed lightweight convolutional excitation module (CEM) is used to obtain the contextual information of targets and reduce the loss of information for small targets. In addition, the C3k2 module in the neck fusion network part, where C3k = True, is replaced by the Convolutional Attention Module with Ghost Module (CAF-GM). This not only reduces the model complexity but also acquires more effective information. The Simple Attention module (SimAM) used in it not only suppresses redundant information but also has zero impact on the growth of model parameters. Finally, the Inner-Complete Intersection over Union (Inner-CIOU) loss function is employed, which enables better localization and detection of small targets. Extensive experiments conducted on the DOTA and VisDrone2019 datasets have demonstrated the advantages of the proposed enhanced model in dealing with small objects in aerial imagery. Full article

Non-small cell lung cancer (NSCLC) is a predominant form of lung cancer that is often diagnosed at an advanced metastatic stage. The processes of cancer cell migration and invasion involve epithelial-to-mesenchymal transition (EMT), which is crucial for metastasis. Targeting cancer aggressiveness with effective plant compounds has gained attention as a potential adjuvant therapy. Eurycomanone (ECN), a bioactive quassinoid found in the root of Eurycoma longifolia Jack, has demonstrated anti-cancer activity against various carcinoma cell lines, including human NSCLC cells. This study aimed to investigate the in vitro effects of ECN on the migration and invasion of human NSCLC cells and to elucidate the mechanisms by which ECN modulates the EMT in these cells. Non-toxic doses (≤IC20) of ECN were determined using the MTT assay on two human NSCLC cell lines: A549 and Calu-1. The results from wound healing and transwell migration assays indicated that ECN significantly suppressed the migration of both TGF-β1-induced A549 and Calu-1 cells. ECN exhibited a strong anti-invasive effect, as its non-toxic doses significantly suppressed the TGF-β1-induced invasion of NSCLC cells through Matrigel and decreased the secretion of MMP-2 from these cancer cells. Furthermore, ECN could affect the TGF-β1-induced EMT process in various ways in NSCLC cells. In TGF-β1-induced A549 cells, ECN significantly restored the expression of E-cadherin by inhibiting the Akt signaling pathway. Conversely, in Calu-1, ECN reduced the aggressive phenotype by decreasing the expression of the mesenchymal protein N-cadherin and inhibiting the TGF-β1/Smad pathway. In conclusion, this study demonstrated the anti-invasive activity of eurycomanone from E. longifolia Jack in human NSCLC cells and provided insights into its mechanism of action by suppressing the effects of TGF-β1 signaling on the EMT program. These findings offer scientific evidence to support the potential of ECN as an alternative therapy for metastatic NSCLC. Full article

In order to solve the problem of small and dense targets in drone aerial images, a small target detection model based on dual branches and adaptive feature fusion is proposed. The model first constructs a small target detection framework with dual branches to improve the detection accuracy while reducing the number of parameters. Secondly, the model introduces semantic and detail injection (SDI) in the neck network and embeds bidirectional adaptive feature fusion in the detection head to innovate and optimize the feature fusion mechanism, achieve the full interaction of deep and shallow information, enhance the feature representation of small targets, and overcome the problem of scale inconsistency. Finally, in order to focus on the target area more accurately, we introduce the large separable kernel attention mechanism into the convolutional layer to provide it with a richer and more comprehensive feature representation, which significantly improves the detection accuracy of targets of different scales. The experimental results show that the model algorithm performs well in the VisDrone2019 dataset. Compared with the original model, the mAP50 of this model increases by 20.9%, the mAP50–95 increases by 23.7%, and the total number of parameters decreases by 61.3%, making it more suitable for drones. Full article