Search Results (692)

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

Reliable dairy goat detection in barns is challenging because uneven illumination, railings, and animal overlap weaken RGB boundaries. We developed DRG-LiteStar-YOLO, a lightweight RGB-camera-compatible detector that integrates monocular pseudo-depth with RGB features without requiring a dedicated depth sensor. DA3-Small was used to generate relative pseudo-depth maps, which were fused with RGB features through reliability-guided fusion and boundary-enhanced multi-scale aggregation. The dataset comprised 1521 images of 134 lactating Saanen goats and 13,985 annotated instances. On the held-out test set, DRG-LiteStar-YOLO achieved a precision of 0.961, a recall of 0.941, an mAP@0.5 of 0.978, and an mAP@0.5:0.95 of 0.735. Compared with RGB-only LiteStar-YOLO, the proposed method improved mAP@0.5 and mAP@0.5:0.95 by 1.6 and 3.8 percentage points, respectively. The five-seed cumulative ablation further showed that the full configuration achieved $0.734\pm 0.002$ mAP@0.5:0.95 compared with $0.697\pm 0.004$ for RGB-only LiteStar-YOLO. The detector contains 4.20 M parameters and 12.60 GFLOPs and achieves 86.2 FPS on an RTX 4090 with precomputed pseudo-depth maps. These results demonstrate that reliability-guided pseudo-depth fusion improves goat localization under complex barn conditions while retaining a compact detector design. DRG-LiteStar-YOLO provides an effective perception framework for RGB-camera-based dairy goat monitoring and supports future counting, tracking, and behavior-analysis applications. Full article

Accurate perception of eggplant fruits and stems remains challenging for greenhouse harvesting robots because illumination changes, foliage occlusion, fruit overlap, and background branches can degrade target visibility, particularly for small and curved stems. To improve joint fruit-and-stem detection under these conditions, this study develops an enhanced YOLOv8n model using a greenhouse dataset collected across different illumination levels, viewpoints, occlusion degrees, and fruit-overlap situations. The baseline network was modified in three aspects. Selected conventional convolutions in the backbone and neck were replaced by Omni-Dimensional Dynamic Convolution (ODConv) to improve feature adaptation to targets with different scales and shapes. Efficient Multi-Scale Attention (EMA) was placed after the SPPF module to emphasize informative responses from fruit and stem regions while reducing background interference. In addition, C2f_MSBlock was incorporated into the neck to strengthen multi-scale feature representation and fusion. The resulting model achieved 96.4% precision, 97.2% recall, 99.0% mAP@0.5, and 86.0% mAP@0.5:0.95, with 3.74 M parameters, 6.5 GFLOPs, and a model size of 7.9 MB. Relative to the original YOLOv8n, these four detection metrics increased by 2.2, 0.3, 0.5, and 2.9 percentage points, respectively, while GFLOPs decreased by 16.7%. These results indicate that the modified model improves detection robustness in complex greenhouse scenes while maintaining moderate computational requirements, providing a feasible visual perception approach for eggplant fruit recognition and stem localization in robotic harvesting. Full article

Background: Early detection of dementia is essential for timely intervention, yet existing diagnostic approaches remain costly, invasive, or dependent on specialized expertise. Electroencephalography (EEG) offers a non-invasive and accessible alternative; however, purely data-driven deep learning models may overlook clinically established neurophysiological biomarkers, particularly in the challenging detection of mild cognitive impairment (MCI). Methods: We propose the Clinical EEG Feature-Augmented Network (CEFA-Net), a knowledge-guided deep learning framework that systematically integrates automatic representation learning from raw multichannel EEG with clinically validated neurophysiological biomarkers. The architecture combines three complementary convolutional pathways capturing multi-scale temporal dynamics with domain-informed feature representations, enabling both data-driven discovery and clinically grounded interpretation. Task-specific optimization strategies—including focal loss, class-aware augmentation, and validation-guided ensemble weighting—were employed to enhance robustness under class imbalance. The model was evaluated on the large-scale the Chung-Ang University Hospital EEG (CAUEEG) dataset (1379 recordings from 1155 patients) across binary abnormality detection and three-class dementia staging tasks. Results: CEFA-Net achieved 81.02% accuracy (macro F1: 81.15%) for dementia staging and 87.15% accuracy (macro F1: 87.41%) for abnormality detection, outperforming baseline methods by 6.75–9.10 percentage points (p < 0.001). Notably, the proposed framework substantially improved MCI detection (F1-score: 78%), representing a 14-point gain over traditional machine learning approaches. Ablation analyses confirmed that clinical biomarker integration and multi-model fusion provide complementary diagnostic value. In an additional patient-disjoint evaluation using the no-overlap partitions, CEFA-Net achieved 85.40% accuracy for abnormality detection and 73.80% accuracy for dementia staging, demonstrating generalization to subjects completely excluded from the training data. Conclusions: These findings demonstrate that knowledge-guided integration of clinical biomarkers with deep representation learning can significantly enhance EEG-based dementia detection. CEFA-Net offers a clinically aligned and computationally efficient solution, supporting its potential for real-world screening and early diagnostic workflows. Full article

Reliable localization and mapping are critical for intelligent robotic systems operating in dynamic indoor environments, where pedestrians and other moving objects can lead to erroneous feature associations, map contamination, and accumulated trajectory drift. To address these challenges, this study proposes the Boundary-Protected Semantic-Geometric Dynamic-Probability (Boundary-SGDP) framework, an enhanced red–green–blue-depth (RGB-D) visual simultaneous localization and mapping (SLAM) system based on boundary-protected semantic–geometric dynamic-probability estimation. The proposed method combines instance-level semantic priors generated by the YOLO26n-seg detector, a segmentation-oriented model in the You Only Look Once (YOLO) family, and the Segment Anything Model 2 (SAM2) with morphological region decomposition and RGB-D depth-edge detection. Potentially dynamic regions are further divided into dynamic interiors, semantic boundary protection bands, and geometrically informative depth-edge regions. Semantic and geometric cues are integrated to estimate a dynamic score for each feature, which is subsequently propagated to the MapPoint level as a dynamic probability. During pose optimization, these probabilities are used to adaptively adjust the weights of reprojection constraints, thereby reducing the influence of motion-contaminated observations while preserving geometrically valuable features around object boundaries and occlusion regions. Unlike conventional hard semantic masking strategies, Boundary-SGDP provides a soft and adaptive mechanism for handling dynamic observations. Experiments conducted on four dynamic walking sequences from the TUM RGB-D benchmark demonstrate that the proposed method achieves lower absolute and relative trajectory errors than the original ORB-SLAM3 system, while retaining substantially more boundary-related features. The results confirm the effectiveness of semantic–geometric fusion and boundary protection for robust visual localization and mapping in dynamic indoor scenes, and demonstrate the potential of the proposed framework for practical autonomous navigation and intelligent perception applications. Full article

This paper proposes a Difference-guided Detail Enhancement Fusion Network (DDEF-Net) for UAV-based RGB–thermal (RGB-T) object detection, which enables effective complementary exploitation of visible and infrared information in complex scenarios. A Difference-guided Kolmogorov–Arnold Network (KAN) Calibration Fusion module (DKCF) is designed to explicitly model cross-modal discrepancies and incorporate KAN-based nonlinear calibration, improving the selection of informative features and reducing redundant feature interference during multimodal fusion. Furthermore, a Scharr–Fourier Detail Enhancement module (SFDE) is introduced to jointly leverage Scharr edge priors and Fourier-domain information to strengthen low-level visible feature representations and preserve fine-grained structural cues. On the DroneVehicle dataset, DDEF-Net achieves 84.9% mAP@0.5 and 72.6% mAP@0.5:0.95, improving the RGB–IR baseline by 3.5 and 4.0 percentage points, respectively, with 4.4 M parameters and 12.5 GFLOPs. An additional experiment on the VEDAI visible–near-infrared (NIR) aerial dataset after dataset-specific training provides supplementary evidence that the proposed modules remain beneficial under a different paired multimodal imaging setting. Corruption experiments show improved robustness to Gaussian and motion blur, whereas the model remains sensitive to strong Gaussian noise. Full article

Accurate weed stem localization is essential for site-specific weed control, including precision spraying, laser weeding, and other targeted weed-control operations. To address species diversity, morphology, and costly multiclass annotation in maize seedling fields, this study proposes a two-stage method based on crop-region exclusion. First, MSDNet, a lightweight YOLOv8n-based maize detector integrating ShuffleNetV2, enhanced feature fusion, coordinate attention, and Wise-IoU loss, detects maize seedlings; pixels within the detected boxes are set to zero. Second, hue–saturation–value thresholding, morphological processing, and area filtering extract vegetation and suppress soil noise. Principal component analysis determines each weed contour’s principal axis, and the image-moment centroid is projected onto this axis to estimate the stem center. MSDNet achieved a mean average precision of 93.4% at an intersection-over-union threshold of 0.5, 8.7 percentage points above the baseline, while reducing parameters by 28.66%. Vegetation segmentation achieved a mean pixel accuracy of 97.6% and a mean intersection over union of 93.8%. Within a 15-pixel tolerance (9.50 mm), stem detection rate and localization precision reached 90.1% and 92.5%, respectively, with a mean localization error of 10.65 pixels (6.74 mm). The proposed method provides visual perception and target-localization support for site-specific weed control while reducing reliance on fine-grained multiclass annotation and species-specific models. Full article

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

UAV-based 3D object detection is important for remote-sensing applications such as autonomous inspection, search and rescue, aerial mapping, flight cooperation, and scene-level environmental understanding, where a detector must localize diverse objects in large and sparsely observed point clouds. Existing 3D object detectors provide a strong foundation, but most of them are developed around autonomous-driving scenarios and are not fully adapted to UAV scenes. Compared with road scenes, UAV point clouds usually cover larger areas, contain more diverse object categories, and include many small, sparse, and structurally varied targets. Many small targets therefore occupy only a few BEV cells and contain limited point returns; subsequent feature aggregation and downsampling can further smooth these sparse local responses, making object boundaries and center-related responses less distinguishable from the background. We propose CenterPoint-UAV, an end-to-end voxel-based detector that refines BEV features for UAV-based 3D object detection. CenterPoint-UAV introduces Context-Detail BEV Enhance (CDBE), which uses a Context Enhancement Branch (CEB) and a Detail Enhancement Branch (DEB) to produce complementary BEV feature maps and fuses them using Adaptive Residual Fusion (ARF). It then uses Cross-Level BEV Fusion (CLBF) to combine early BEV details with deep semantic features, followed by a Fine Center Head (FCH) for denser center prediction. Experiments on WiSAR3D, a large-scale real-world UAV point-cloud dataset for multi-category object detection, show that CenterPoint-UAV achieves state-of-the-art mAP among existing methods and maintains a low parameter budget, demonstrating its effectiveness for UAV-based 3D remote sensing. Full article

During cherry-tomato-bunch harvesting, clustered fruits, thin stems, and ambiguous fruit–stem junctions cause missed detections and errors in picking-point localization. To address these challenges, we propose TSP-Net, a structure-aware and geometry-constrained detection network built on YOLOv11n, together with an ROI heat-map regression module for picking-point localization. TSP-Net integrates the Truss-aware Multi-scale Ghost Cross Stage Partial (TMG-CSP) module into the backbone to strengthen structural features of fruit edges, small stems, and clusters. It introduces the Truss-oriented Axial-Local Attention (TALA) module during feature fusion to capture fruit-arrangement direction and local occlusion boundaries. We also design Physics-informed Truss Consistency (PTC) Loss to regularize detections by enforcing consistency with fruit-cluster morphology through constraints on center distribution, inter-fruit spacing, and scale continuity. For picking-point localization, local ROIs are extracted from detection outputs, Gaussian heat maps are predicted, and continuous coordinates are decoded using soft-argmax. In the controlled evaluation, TSP-Net attains precision 81.81%, recall 73.21%, mAP@50 79.53%, and mAP@50:95 64.83%, which exceed the corresponding YOLOv11n results by 0.83, 0.32, 1.29, and 1.03 percentage points, respectively. The model size is reduced from 5.23 MB to 5.08 MB, the parameter count falls from 2.59 M to 2.49 M, and computational cost declines from 6.4 to 6.0 GFLOPs. On the common valid-ROI test set, the proposed heat-map localization yields a mean localization error of 47.2 px, a median error of 24.2 px, and PCK@20, PCK@50, and PCK@100 of 45.1%, 68.4%, and 85.5%, respectively. Under identical ROI conditions, this heat-map method reduces the mean localization error by 11.3 px and raises PCK@20 by 14.9 percentage points compared with direct coordinate regression. In summary, the proposed approach enhances cherry-tomato-bunch detection and 2D picking-point candidate localization while meeting lightweight constraints, offering a structure-aware visual perception solution for greenhouse cherry-tomato harvesting. Full article

Small-scale pedestrians in UAV imagery often exhibit limited pixel coverage, weak texture, and severe background interference, while progressive network downsampling can further degrade their short-side structures and increase missed detections. To address this problem, we propose DGF-YOLO, a degradation-guided feature enhancement method built on YOLOv12n. The method introduces a degradation-level criterion to identify the feature stage at which a pedestrian first undergoes significant structural degradation and, based on the resulting statistics, incorporates a high-resolution P2 detection head. It further employs a Directional Structure-Aware module to enhance local, horizontal, and vertical structural cues through adaptive multi-branch fusion, a Degradation-Guided Attention module to learn a degradation guidance map under explicit supervision and reweight degradation-sensitive regions, and a Fine-Grained Structure Preservation module to retain local contours and contextual details using depthwise and dilated convolutions. On the single-class pedestrian detection task constructed from VisDrone2019-DET, DGF-YOLO achieves 70.4% precision, 51.8% recall, 59.7% mAP50, and 26.9% mAP50-95, improving the YOLOv12n baseline by 6.6, 7.4, 10.3, and 6.5 percentage points, respectively. The results suggest that the proposed feature enhancement strategy helps reduce missed detections associated with structural degradation in small-scale pedestrians. Full article

To address the challenges of insufficient feature representation and the difficulty of detecting sparse and distant objects in UAV-borne LiDAR point clouds—which exhibit significantly lower point density than terrestrial/mobile LiDAR scans—this paper proposes an enhanced detection algorithm built upon the PointPillars framework. First, a coordinate attention mechanism is incorporated to enhance the network’s ability to capture spatial geometric information. Furthermore, the backbone network is redesigned with a dual-path structure and a feature modulation fusion module, enabling adaptive integration of multi-scale features. Experimental evaluations conducted on a custom simulated UAV-borne LiDAR point cloud dataset demonstrate that the proposed method achieves 85.35% 3D mAP and 88.26% BEV mAP, corresponding to absolute improvements of 21.61 and 7.85 percentage points compared with the original PointPillars model. In addition, the proposed approach demonstrates consistent performance on the publicly available KITTI benchmark through preliminary cross-dataset validation. The results indicate that the proposed method can effectively improve the detection accuracy and robustness of LiDAR-based 3D object detection in complex environments. Full article

Infrared and visible images provide complementary cues for maritime ship detection, but practical dual-sensor systems often produce image pairs that are not strictly registered. Directly fusing such unregistered pairs may introduce ghosting artifacts, blurred target boundaries, and feature conflicts, especially over weak-texture sea surfaces where reliable correspondence cues are sparse. To address this problem, we propose UA-FusionDet, an unregistered-aware infrared-visible fusion framework with cross-modal feature alignment for maritime ship detection. The proposed framework extracts visible and infrared features with a dual-branch encoder, aligns the visible feature to the infrared reference through cross-modal deformable feature alignment, and suppresses unstable background offsets using sea-surface saliency guidance. Wavelet-guided complementary fusion then decomposes the aligned features into low- and high-frequency sub-bands, enabling frequency-aware fusion of infrared thermal saliency and visible structural details before feeding the fused representation to both a lightweight reconstruction decoder and a ship detection head. The reconstruction decoder provides auxiliary image-level regularization, while the detection branch supervises the task-oriented fused representation with ship bounding-box annotations. UA-FusionDet does not require registration ground truth or fused-image ground truth during training, making it suitable for realistic maritime monitoring scenarios with imperfectly aligned visible and infrared sensors. Experiments on 3132 unregistered visible–LWIR maritime image pairs show that UA-FusionDet achieves a precision of 0.904, a recall of 0.866, an ${\mathrm{mAP}}_{50}$ of 0.912, and an ${\mathrm{mAP}}_{50–95}$ of 0.566, exceeding the strongest competing method by 2.5 and 2.4 percentage points on the two mAP metrics, respectively, while maintaining an inference speed of 52.6 FPS. These results demonstrate that the proposed alignment and fusion framework improves detection accuracy under cross-modal misregistration while retaining practical inference efficiency. Full article

Augmented reality (AR) enhances the real world scene by overlaying virtual information onto it. Vision-based 3D tracking and registration is the key technology for ensuring the fusion of virtual and real content in monocular AR systems. Existing mainstream visual tracking and registration methods are susceptible to illumination variations, motion blur, target occlusion, and dynamic background interference in complex scenarios. They also suffer from low computational efficiency, cumulative pose errors, and insufficient stability, making them difficult to deploy on low power edge devices such as embedded systems and mobile terminals. To address these issues, this paper proposes a lightweight monocular AR 3D tracking and registration method that integrates ORB-FREAK features, mismatching outlier filtering, background weighted mean shift, and template-based relocalization. The method does not rely on depth sensors or neural network inference, enabling efficient and accurate lightweight pose estimation. Specifically, we first combine the ORB (Oriented FAST and Rotated BRIEF) descriptor with the FREAK (Fast Retina Keypoint) algorithm for feature detection and initial matching. Hamming distance is used for coarse filtering of mismatched point pairs, and an ascending sort combined with an iterative sequential sampling strategy is applied to solve the optimal homography matrix, significantly improving the accuracy and efficiency of matrix estimation. Then, distance constraints among feature points are imposed on the target registration region to optimize the selection, and camera pose is computed based on the matching between 2D feature points and their corresponding 3D spatial coordinates, eliminating the error accumulation problem of conventional algorithms. Real-time feature matching is further used to correct the optical flow tracking sequence and camera pose, ensuring the continuity of the AR tracking process. Finally, a background weighted mean shift algorithm is introduced to narrow the feature detection range and suppress background interference, complemented by a template-matching relocalization module and a dynamic model update strategy, which effectively enhance the robustness of continuous tracking and registration under complex conditions. Experimental results demonstrate that, in extreme scenarios such as low light conditions, high speed motion, and occlusion, the proposed method achieves AR 3D tracking and registration success rates of 86.7%, 82.3%, and 78.5%, respectively. It exhibits superior performance in pose estimation accuracy and anti-interference capability in complex environments, with significantly reduced computational overhead. Moreover, it can achieve robust and continuous AR 3D tracking and registration on low power edge devices, effectively adapting to demanding AR application scenarios and providing reliable technical support for lightweight AR applications. Full article

Surface cracks are critical indicators of deterioration in flood-control infrastructure, yet automated detection from inspection imagery remains challenging due to complex backgrounds, elongated geometries, and variations in apparent scale. This study aims to develop a lightweight detector for accurate dike crack detection while maintaining computational efficiency. A hybrid CNN–Transformer detector, termed YOLO-Defender, was built on YOLOv8n. The proposed framework incorporates a C2F-FTB block in the backbone for local–global feature representation, a BiFF-PAN neck for enhanced multi-scale feature fusion, and coordinate attention for improved spatial feature localization. The model was evaluated on an in-house dike crack dataset containing 1217 images collected from flood-defense structures and related crack imaging scenarios using a unified training and testing protocol. YOLO-Defender achieved 89.6% mAP@0.5 and 69.2% mAP@0.5:0.95, improving YOLOv8n by 3.0 and 5.1 percentage points, respectively. Compared with YOLOv8n, the proposed model reduced parameters and GFLOPs by 41.2% and 18.5%, respectively, while achieving an inference speed of 153 FPS on a desktop GPU. The results indicate that task-oriented architectural design can improve crack detection accuracy and localization performance while preserving lightweight characteristics. These findings support efficient screening of hydraulic infrastructure inspection imagery. Full article