Search Results (413)

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Imaging-based spatial transcriptomics has advanced from low-plex single-molecule fluorescence in situ hybridization to a diverse set of highly multiplexed platforms, with recent multimodal and pathology-compatible capabilities. Despite major differences in chemistry, coding, and imaging strategies across different platforms, their biological interpretation often converges on a few notable computational biology problems. This review examines imaging-based spatial transcriptomics through the lens of data interpretation and applications, focusing on the analytical framework that converts raw fluorescence signals or accompanying in situ sequencing data into molecule-, cell-, and tissue-level representations. We discuss the key challenges in preprocessing, registration, restoration, feature detection, barcode decoding, molecule calling, cell segmentation, transcript assignment, probabilistic cell typing, spatial-domain inference, and atlas integration. We also highlight how optical crowding, tissue thickness, panel bias, and multimodal complexity increase computational difficulty. Finally, we summarize applications of imaging-based spatial transcriptomics techniques, ranging from subcellular RNA localization to atlas-scale and pathology-aware spatial analysis. Full article

RGB-T pedestrian detection remains challenging under low-light, occluded, crowded, and complex-background conditions. To improve cross-modal feature fusion while maintaining model efficiency, this paper proposes MCFusion, a lightweight RGB-T pedestrian detection method with progressive thermal compensation. MCFusion adopts a dual-branch RGB–thermal feature extraction structure and introduces a Modality-Compensated Gated Fusion (MCGF) module at the P4 and P5 semantic stages, which is implemented as a zero-initialized residual compensation mechanism. MCGF uses RGB features as the primary stream and progressively compensates them with thermal auxiliary features through a zero-initialized convolutional gate, reducing the interference caused by direct fusion. In addition, a Lightweight Shared Convolutional Detection Head (LSCD) is adopted to reduce redundant computation in multi-scale prediction. On the LLVIP dataset, MCFusion achieves 95.30% mAP₅₀ and 60.10% mAP_50:95 with 5.21 M parameters and 10.50 GFLOPs. Compared with the YOLOv11n RGB baseline, it improves mAP₅₀ and mAP_50:95 by 7.50 and 10.70 percentage points, respectively. Experiments on KAIST, ablation studies, and visualization results further demonstrate the effectiveness of the proposed method. Full article

In situ hybridization (ISH) has undergone a rapid evolution from a low-throughput histological staining technique to a diverse family of modern methods for sensitive, specific and multiplexed molecular detection in intact cells and tissues, and to a cornerstone technology for image-based spatial transcriptomics. This transformation has been driven by advances in probe design, signal amplification, cyclic imaging, combinatorial barcoding, automated fluidics and computational decoding, which together allow RNA molecules to be measured within preserved cellular and tissue architecture. In this review, we examine the molecular and engineering principles that underlie modern ISH methods and their extension into ISH-based spatial profiling, with emphasis on hybridization chain reaction, branched-DNA amplification, SABER-FISH, rolling-circle-amplification-based approaches, seqFISH, MERFISH, RAEFISH and selected commercial implementations. We discuss how sensitivity, specificity, tissue compatibility, optical crowding, imaging burden, cost, reproducibility and computational uncertainty shape the practical use of each method. Sequencing-based spatial capture platforms are not reviewed comprehensively, but are considered where comparative benchmarks help clarify trade-offs in spatial resolution, transcriptome breadth, tissue area or analytical interpretation. We also consider how recent benchmarking and standardization efforts are beginning to define quantitative criteria for comparing platforms, and how advances in segmentation, barcode decoding, spatial integration and cell–cell communication analysis convert raw images into biological insight. Finally, we highlight applications in targeted transcript detection, tissue-based validation, neuroscience, cancer, developmental biology, non-model organisms and spatial functional genomics, where modern ISH methods and ISH-based spatial profiling provide information that bulk and dissociated single-cell approaches cannot capture. Together, these developments trace how ISH has expanded from targeted molecular visualization into a broad methodological framework for in situ detection and spatially resolved transcriptomic analysis. Full article

Intelligent perception of watermelon targets in complex field environments is a key prerequisite for automated harvesting and future collaborative decision-making with agricultural large models. To address severe leaf occlusion, large pose variation, dense adhesion among adjacent fruits, and the inability of conventional horizontal bounding boxes to accurately represent target orientation under natural cultivation conditions, this paper proposes an improved YOLO11n-OBB-based method for rotated watermelon detection. During data preparation, a semi-automatic annotation strategy combining segmentation-mask assistance with circumscribed rectangle fitting was adopted to efficiently construct a watermelon OBB dataset that closely matches the true physical boundaries of the fruits. On this basis, three structural improvements were introduced to the YOLO11n-OBB baseline: an LSK module was selectively embedded into the middle and later stages of the backbone to enhance adaptive receptive-field modeling and occlusion reasoning in complex bac kgrounds; the original neck structure was replaced with a lightweight BiFPN to strengthen bidirectional feature fusion for targets with large-scale variation in field scenes; and KFIoU Loss was incorporated into the rotated box regression branch to alleviate angle sensitivity and boundary discontinuity, thereby improving the convergence stability of orientation parameter learning. On the constructed watermelon OBB test set, the improved model raised mAP@0.5 (OBB) from 0.871 to 0.931, mAP@0.5:0.95 (OBB) from 0.670 to 0.736, Precision from 0.885 to 0.931, and Recall from 0.849 to 0.908 relative to the YOLO11n-OBB baseline (relative gains of 6.89%, 9.85%, 5.20%, and 6.95%, respectively), while keeping the inference speed at 100 FPS and the parameter count at only 2.71 M. While maintaining a compact model size and high real-time performance, the proposed method significantly improved rotated detection accuracy in crowded and overlapping scenes. In addition, the detection results were encapsulated into a structured JSON perception interface, preliminarily demonstrating the integration pathway of this lightweight front-end for task planning and human–machine collaborative operations with agricultural large models, and indicating its potential for future intelligent agricultural decision-making. Full article

In all-weather Earth observation and complex unstructured environments, traditional single-modal remote sensing object detection often fails due to low illumination and strong background interference. While RGB–infrared fusion provides complementary information, existing methods are typically computationally intensive and struggle with dense small objects and modality discrepancies, limiting their deployment on resource-constrained platforms. To address these challenges, we propose ISDG-Net, a lightweight and efficient visible-infrared dual-modal object detection framework specifically tailored for edge deployment. ISDG-Net integrates four core components: (1) a channel-separated inverted bottleneck backbone (IBC-Conv) that reduces parameter redundancy while preserving modality-specific semantics; (2) a dynamic sparse attention module (DySparse) based on Bi-Level Routing Attention, enabling long-range dependency modeling with low computational cost; (3) an adaptive spatial fusion detection head (Detect-SASD) that aligns visible and infrared features at the pixel level to resolve semantic inconsistency and scale mismatch; and (4) a geometry-aware IoU selector (GIS) that mitigates over-suppression in crowded scenes by incorporating multi-dimensional geometric constraints into post-processing. Extensive experiments on the VEDAI, M3FD, and LLVIP datasets demonstrate the effectiveness and efficiency of ISDG-Net. It achieves 55.1% and 77.1% mAP@0.5 on VEDAI and M3FD, respectively, and 93.7% mAP@0.5 with 89.7% recall on LLVIP, while maintaining a compact model size of 4.2 M parameters and 11.3 GFLOPs. These results validate that accurate RGB–infrared detection is achievable under strict resource constraints, making ISDG-Net well-suited for deployment in edge-based remote sensing systems. Full article

Although lightweight deep learning models have shown promise for livestock monitoring, there is still limited evidence regarding their comparative performance and practical deployment under real broiler production conditions characterized by high stocking density, severe occlusion, and constrained computational resources. In this context, the present study aimed to evaluate three lightweight object detection architectures for broiler monitoring and to determine their suitability for low-cost edge deployment in settings relevant to small and medium-sized producers. A novel dataset, publicly released through Zenodo to support reproducibility, was constructed from images acquired in both a prototype farm and a high-density commercial facility. These environments captured the visual complexity of intensive broiler production, where overlapping individuals and frequent occlusion challenge detection performance. YOLOv10s, Faster R-CNN, and EfficientDet-D0 were trained and evaluated for detection accuracy and computational efficiency. YOLOv10s achieved the best results, with a mean Average Precision (mAP) of 0.95, whereas Faster R-CNN and EfficientDet-D0 were less suitable for crowded scenes due to region proposal saturation and limited feature-extraction capacity. The selected model was further implemented on a Raspberry Pi 5, achieving a stable latency of 392.17 ms. These results demonstrate that YOLOv10s provides a robust balance between accuracy and efficiency for local broiler monitoring on affordable hardware, while also indicating that active thermal management is necessary to maintain operational stability under real-world conditions. Full article

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance. Full article

With the rapid advancement of autonomous driving in modern society, dense pedestrian detection technology has encountered performance bottlenecks. To address this, we propose a robust and lightweight pedestrian detection algorithm, RMP-YOLO, designed to efficiently detect small, occluded, and low-light objects. Firstly, RFAConv is utilized as the core component of the backbone network, combining standard convolution with attention mechanisms and using group convolution to extract features from the spatial receptive field. Secondly, MobileViTv3 is introduced into the backbone to combine CNNs with Transformers. The model is further enhanced by adjusting feature fusion, introducing residual connections, and optimizing local representation with deep convolutional layers. Finally, the PIoUv2 loss function is employed for bounding-box regression, significantly reducing detection errors for small-scale pedestrians in crowded environments. Experimental results demonstrate that RMP-YOLO improves mAP@0.5 by 1.3% on a custom dataset and 0.91% on the WiderPerson dataset. Crucially, it maintains high efficiency with only 3.71 million parameters and 6.29 GFLOPs, meeting the deployment requirements for low computational power and high precision. Full article

Small-object detection in remote sensing imagery faces two specific challenges that existing lightweight detectors fail to address jointly: the irreversible loss of high-frequency boundary cues during repeated downsampling, and feature smearing between neighboring instances caused by uniform multi-scale fusion. This paper presents SFD-Net, a spatial–frequency adaptive network designed to explicitly address these two limitations for aerial imagery. A backbone network and a spatial–frequency adaptive neck are used in the proposed model. Wavelet-based downsampling is applied in the backbone to reduce aliasing while preserving high-frequency information. The direction-sensitive aggregation is incorporated to better capture oriented structural patterns. In the neck, asymmetric and scale-dependent feature routing is introduced to enhance shallow boundary cues, improve instance separation in crowded regions, and limit interference from deep semantic features. Experiments on the VisDrone-DET2019, UAVDT, SIMD, and NWPU VHR-10 datasets demonstrate that SFD-Net achieves a favorable balance between detection accuracy and computational cost. In particular, on the SIMD dataset, SFD-Net achieves 82.2% mAP@0.5 and 66.7% mAP@0.5:0.95 with only 3.4 M parameters and 8.3 GFLOPs. These results indicate that the proposed method is an effective and parameter-efficient solution for remote sensing small-object detection, especially in resource-constrained deployment scenarios. Full article

Automated detection and tracking of individual sheep are essential for precision livestock farming. However, existing approaches face significant challenges: (1) Limited dataset diversity with predominant aerial perspectives; (2) Detection failures under severe occlusions; (3) Frequent ID switches due to high appearance similarity. To address these challenges, our paper presents an integrated framework. Firstly, we construct a multi-scene indoor sheep dataset with diverse environmental conditions. Secondly, for detection, we propose an improved YOLOv8 incorporating SheepNMS and Flock-aware Localization Loss (FL-Loss) to handle crowded scenarios and occlusion. Finally, for tracking, we enhance BoT-SORT with a Flock Appearance Module (FAM) and Trajectory Correction Module (TCM) for robust association and drift mitigation. Extensive experiments demonstrate measurable improvements in detection accuracy, tracking consistency, and reductions in ID switches and fragmentations across diverse monitoring scenarios. Full article

Automated detection of suspicious human activities in complex and crowded environments remains a critical challenge in modern surveillance systems due to high false-positive rates, poor contrast and generalization across diverse scenes. We propose a GM_CNN3D Model for the classification of suspicious activity based on a Deep Fused Feature Block (DFFB) framework that integrates handcrafted spatial descriptors (PCA-HOG and Motion-HOG) with deep spatiotemporal features extracted from 3D Convolution Neural Network (3D-CNN). Motion regions are first localized using a Gaussian Mixture Model (GMM), after which handcrafted and deep features are concatenated in a dimensionality-normalized fusion stage, followed by a fully connected layer and softmax classification. The system is evaluated on five diverse and publicly available datasets: Violent Crowd, Hockey Fight, Kaggle Fight, Movies Fight, and Custom Annotated YouTube Clips, achieving up to 99.12% accuracy, 98.7% F1-score, and a ROC-AUC of 0.992, outperforming state-of-the-art CNN, LSTM, and SlowFast models. All datasets include real world scenarios with varying lighting, crowd density, and camera viewpoints, with annotations created manually where unavailable. The proposed method demonstrates robust cross-scene performance, enabling automated alarming and reduced false positives in real-time security operations. Full article

Efficient extraction of building footprints from aerial and satellite imagery is essential for urban planning, infrastructure management, and large-scale geospatial analysis. Traditional raster-based approaches provide limited geometric precision, while existing polygon-generation methods often rely on detecting and ordering small-scale building vertices, which can lead to incomplete structures, distorted shapes, and high computational cost. To address these limitations, this study proposes an Edge-Attentive Dual-Branch Frame Field Network (EA-DBFFN) for automated and high-precision building polygon extraction. The method is built upon frame field learning and introduces a dual-branch architecture that separately predicts building masks and edges. A Dual-Task Decoder enlarges and adapts receptive fields while applying spatial attention to enhance the representation of structural details. Fixed Sobel and Laplacian filters are incorporated to strengthen boundary detection. In addition, a Dual-Task Mutual Guidance Module promotes the exchange of complementary information between the mask and edge branches, improving geometric consistency and reducing boundary errors. Experiments conducted on the Inria Aerial dataset and the CrowdAI dataset demonstrate that EA-DBFFN achieves superior performance in region-based metrics, with an AP75 of 72.9% on CrowdAI, representing a 2.3% improvement over competing methods. Furthermore, EA-DBFFN produces geometrically higher-quality polygons, with the Max Tangent Angle error reduced by 6.4%, the Invalid Polygon Ratio reduced by 66.3%, and Edge Smoothness improved by 72.7% compared to the best competing method. The results show that EA-DBFFN provides an effective and computationally efficient framework for generating high-quality vectorized building footprints suitable for large-scale urban analysis. Full article

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article

In indoor crowd navigation, quickly and accurately acquiring the kinematic data of pedestrians within a robot’s field of view is a crucial factor determining success. Existing indoor pedestrian tracking methods have limitations in accuracy and real-time performance. To address these issues, a lightweight pedestrian tracking method based on an improved YOLOv10s and DeepSORT is proposed. In the detection stage, a CPNGhostNetV2 module incorporating Ghost Convolution and attention mechanisms is first designed to replace the original C2f module in YOLOv10s. This achieves lightweight while effectively preserving global feature information. Secondly, the GSConv module is introduced to further reduce computational load and model parameters. Finally, the Focal Loss function is introduced to enhance the detection capability of the YOLOv10s model in dense scenes. In the tracking stage, a novel trajectory management mechanism is proposed to reduce the ID-switching problem under occlusion conditions. The experimental results show that the improved YOLOv10s reduces computational complexity by 33.9% and parameters by 17.4% compared to the original model. It also improves mAP@50 by 0.6%. The improved DeepSORT algorithm achieves a 7.0% increase in MOTA, a 1.4% increase in MOTP, and a 24.8% reduction in ID-switch counts compared to the original YOLOv10-DeepSORT. It outperforms traditional algorithms in terms of accuracy, real-time performance, and computational efficiency, demonstrating promising application prospects. Full article