Search Results (151)

Human pose detection is fundamental to quantitative sports training analysis in college general education courses, enabling an objective assessment of college students’ movement quality and the early identification of sports injury risks among non-professional athletes. At present, those detectors based on YOLO have encountered difficulties in capturing the continuous movement patterns of college athletes in routine training, maintaining the regression accuracy of different size posture targets, and maintaining the real-time calculation speed in the campus sports environment. Furthermore, most existing pose-estimation frameworks are optimized for general scenes and fail to address the unique challenges of college physical education settings, including non-standard student movements, diverse skill levels, and strict cost constraints for large-scale deployment. In order to solve these problems, we put forward PPLCNet-YOLOv11, which is a simplified human posture-estimation framework designed for college physical education. This model is optimized by three key improvements: (1) replacing the original backbone network with PPLCNet to enhance feature extraction, while strictly observing the strict FLOPs and parameter restrictions; (2) an enhanced Multi-Scale Attention Mechanism (MSAM) that combines adaptive scale perception, hierarchical channel attention, and pose-sensitive spatial attention to better represent elongated anatomical structures and multi-scale pose cues; and (3) an improved enhanced IoU loss function that incorporates scale-aware and aspect-ratio-aware penalty terms to refine the bounding box adjustment for atypical and sports-specific gestures. Experiments on both a dedicated college student sports pose dataset and two public benchmark datasets (COCO Keypoints 2017 and MPII Human Pose) demonstrate that PPLCNet-YOLOv11 achieves 77.8% mAP@0.5 and 37.09% mAP@0.95 based on the campus dataset, with 82.34% precision and 75.00% recall, while requiring only 2.62 M parameters and 6.38 GFLOPs. Extensive inference speed tests show that the model achieves 127 FPS on an NVIDIA RTX 4090 GPU, 38 FPS on an Intel i7-12700 CPU, and 16 FPS on a Jetson Nano edge device, meeting the real-time requirements of campus sports monitoring. Compared with mainstream lightweight YOLO variants and state-of-the-art specialized pose-estimation models, our proposed method improves mAP@0.5 by 4.93–12.6 percentage points based on the campus dataset. All experiments were repeated five times with different random seeds, and we report mean values with standard deviations and statistical significance tests to ensure result reliability. These results indicate that PPLCNet-YOLOv11 provides an accurate and resource-efficient solution for real-time pose evaluation in college physical training. Full article

The growth of Intelligent Transportation Systems (ITS) has made high-quality perception data from multi-sensor setups essential. Pairing millimeter-wave (mmW) radar with a monocular camera is a common way to recover three-dimensional information about the environment, but aligning the two is difficult because sparse radar point clouds and dense camera images differ sharply in how they sense a scene. The problem grows more severe in roadside infrastructure, where the high mounting elevation introduces perspective distortion that vehicle-mounted systems rarely face. This paper presents a systematic review, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, of radar-camera extrinsic calibration for fixed roadside infrastructure, organizing existing work into a taxonomy that separates traditional two-stage pipelines from recent end-to-end learning frameworks. Because methods designed specifically for roadside units remain scarce, the review also covers vehicle- and robot-mounted methods whose static-sensor formulation carries over to fixed roadside deployment. For the two-stage pipeline, the analysis covers target-based and targetless correspondence registration along with the optimization techniques and algorithmic assumptions behind parameter estimation. The end-to-end learning literature shows a clear shift toward self-supervised and fusion-based models, some of which report real-time performance. The review also compares the metrics and procedures used to quantify calibration accuracy. Progress is evident, but robustness in cluttered urban environments remains an open challenge, and the paper closes by outlining future directions, arguing that standardized roadside benchmarks are needed before scalable, targetless calibration can mature. Full article

Multi-agent perceptual map construction and long-term maintenance constitute an important paradigm for improving adaptability and real-world applicability. With the outstanding capability of 3D Gaussian Splatting in preserving fine-grained texture details, a number of 3DGS-based real-time mapping approaches have recently emerged. However, these methods often struggle to cope with complex dynamics in real-world environments and lack the generalization needed to scale to multi-agent systems. Existing solutions typically rely on direct parameter concatenation or locally confined optimization, which are unable to explicitly model cross-agent observation reliability under temporal asynchrony and dynamic inconsistency, and therefore tend to amplify conflicting updates rather than resolve them. To address these limitations, we propose DGOMapping, an online system for multi-agent dynamic perceptual mapping. DGOMapping leverages an uncertainty-coupled 4DGS scene representation and a collaborative interaction mechanism via Gaussian perception-score exchange, enabling both real-time 4DGS construction and long-term map memory adjustment. Experiments on multiple real-world datasets demonstrate that DGOMapping effectively suppresses dynamic interference and exploits multi-agent collaboration, achieving state-of-the-art performance in both tracking and reconstruction. The proposed system therefore provides a practical sensing-oriented solution for collaborative perception and real-time dynamic environment mapping. Full article

Traffic sign segmentation is a fundamental component of intelligent transportation systems and autonomous driving, where reliable pixel-level perception is required under challenging real-world conditions such as illumination variations, occlusion, scale diversity, and complex urban backgrounds. In this work, we propose Residual–Recurrent Kolmogorov–Arnold Network U-Net (R2KAN-U-Net), where “R2” denotes the integration of residual convolutional learning and recurrent KAN-based feature refinement. The proposed architecture combines residual U-Net feature extraction, multi-scale KAN fusion, and recurrent KAN refinement to improve pixel-level traffic sign segmentation under challenging road-scene conditions. The proposed framework integrates three complementary components: (1) residual convolutional blocks for stable feature propagation; (2) a multi-scale KAN fusion bottleneck for capturing contextual information at different receptive fields; and (3) recurrent KAN refinement modules for iterative enhancement of discriminative features. Unlike conventional convolutional architectures, the proposed KAN-based formulation replaces linear transformations with learnable univariate functions, enabling adaptive nonlinear feature modeling. We conduct extensive experiments on a custom dataset containing 9300 annotated urban traffic scene images, as well as on the ADE20K and Cityscapes benchmarks. On the custom dataset, the proposed R2KAN-U-Net achieved a Dice coefficient of 0.92 and an IoU score of 0.89, providing a strong accuracy–efficiency trade-off for traffic-sign foreground segmentation. It achieves competitive segmentation accuracy compared with recent CNN-, transformer-, and state-space-based segmentation models while using fewer parameters and lower computational cost. Additional low-light experiments demonstrate improved segmentation stability, with R2KAN-U-Net achieving the highest low-light Dice score of 0.88 and a competitive low-light IoU of 0.79. Furthermore, the proposed architecture maintains competitive computational efficiency with only 24 M parameters, 44.8 G FLOPs, and near-real-time inference at 13 ms per image. The experimental results demonstrate that integrating KAN-based function-space learning with residual and multi-scale feature refinement provides an effective and computationally efficient solution for robust traffic sign segmentation in complex driving environments. Full article

Student behavior recognition in classroom environments is important for teaching quality assessment and intelligent education, yet it remains challenging due to dense student distributions, frequent occlusion, substantial scale variation, and the subtle nature of common classroom activities. To address these issues, this paper proposes RepYOLOv5-SF3D, a cascaded visual perception framework for fine-grained student behavior recognition in complex classroom scenes. The framework integrates a lightweight RepYOLOv5m detector with a dual-stream SlowFast-3D recognition branch, enabling automated inference from raw video input to behavior labels. To improve robustness in dense and occluded scenes, the front-end detector serves as a spatial-prior module, while a decoupled training strategy reduces the impact of localization instability on back-end spatiotemporal learning. In addition, two task-oriented modules are introduced in the recognition branch: the Spatiotemporal Depthwise-Separable 3D module (SDS3D) and the Normalization-Based Temporal Attention Mechanism (NTAM). Experimental results on a real classroom dataset show that RepYOLOv5-SF3D achieves a mean average precision (mAP) of 88.83%, outperforming the baseline SlowFast model by 3.36% and surpassing the existing LSTC method by 2.05%, while maintaining a front-end inference latency of 12.5 ms per frame and a total model size of 151.46 MB. These results demonstrate a favorable balance between fine-grained recognition accuracy and edge-deployment efficiency in practical classroom visual sensing. Full article

This study proposes a human-centered multimodal framework for characterizing safety-relevant driver functional domains in professional bus drivers. Unlike conventional approaches that rely on isolated psychological or physical assessments, the proposed framework integrates self-perception, psychomotor performance, and cognitive–perceptual assessment to provide an exploratory, structured characterization of driver-related functional capacities. Eighteen professional bus drivers participated in this study. Self-perception data were obtained from all 18 participants, whereas psychomotor and cognitive–perceptual assessments were completed by 16 participants. These measurements were used to examine multiple domains relevant to driving safety, including behavioral awareness, motor coordination, attention, visual tracking, and hazard-perception-related processing. Given the modest sample size, the study should be regarded as an exploratory pilot investigation. Data were analyzed using a laboratory-based cross-sectional between-subjects design to examine age- and gender-related differences across the assessed domains. The findings suggested that selected age- and gender-related differences and descriptive tendencies were observable across multiple domains. Male drivers descriptively showed higher self-rating scores, female drivers showed different performance tendencies in selected psychomotor tasks, and male drivers demonstrated substantially greater grip strength. Older drivers showed slower and less efficient performance in several cognitive–perceptual measures, with the clearest age-related effect observed in the tachistoscopic traffic test, where older participants showed a higher error tendency under time-constrained traffic-scene processing conditions. The constructs and measures proposed in this study are intended as general laboratory-based assessments of driver-related capabilities rather than direct measures of actual driving performance, real-time driver-state indicators, or validated sensor-based monitoring indicators. As candidate human-factor constructs, they may inform future driver monitoring research by helping clarify how driver-related signals or behaviors could eventually be linked to underlying functional and safety-related meaning in intelligent transportation environments. Full article

Despite their extended detection ranges and superior precision compared with onboard sensors, roadside perception systems suffer from severe occlusion artifacts in complex traffic, causing significant tracking failures and ID switches. To address this, we propose a novel Dynamic Occlusion–Predictive Neural Network tailored to challenging roadside environments. First, we introduce a Transformer-based Dynamic Occlusion State Predictor to explicitly model the temporal evolution of occlusion. Unlike traditional tracking methods, this module continuously forecasts future occlusion ratios for each target by analyzing historical occlusion patterns. Critically, these predictions are integrated into the tracking framework as dynamic weighting factors in the loss function, enabling the model to adaptively penalize tracking errors based on the predicted occlusion severity and significantly enhancing robustness against dynamic occlusion scenarios. Second, leveraging the predicted occlusion states, we propose a GNN-based Spatial Reasoning Module to address trajectory fragmentation. This module constructs a heterogeneous graph integrating road occupancy information and neighboring vehicle poses to infer the existence and motion patterns of targets within occluded regions. By analyzing scene-level physical constraints, it generates motion predictions for invisible targets and links these inferred states to fragmented trajectories, ensuring temporally continuous tracking even during prolonged visual occlusions. Experiments on the DAIR-V2X and our self-collected roadside dataset show that our framework outperforms state-of-the-art methods in precision and robustness, achieving a 5.1% MOTA gain over the best baseline. This advantage peaks under high occlusion, where preserving ID continuity and minimizing failures validates its efficacy for real-world roadside multi-target tracking. Full article

Traffic object detection based on camera sensors is a critical task for autonomous vehicles. However, in nighttime conditions with adverse lighting, several challenges arise: blurred object edges, large-scale variations, and complex lighting conditions involving both overexposure and underexposure. As a result, it remains difficult for vision-based perception tasks to ensure reliable precision and rapid inference simultaneously. This paper proposes a novel, efficient, and lightweight vision module for detecting traffic objects in challenging nighttime environments, developed by enhancing the YOLOv8n architecture. Firstly, a bidirectional weighted feature fusion method (BiFPN) is incorporated in the path aggregation network, and an additional shallow P2 feature map is introduced to fully utilize key information from features at different scales. Then, the coordinate attention (CA) module is inserted between the end of the feature pyramid and the detection head to capture both semantic and spatial information of the object. Finally, the dynamic upsampler (DySample) is employed to guide the model in focusing on the detailed features of challenging samples, thereby balancing accuracy across different object categories. A subset of nighttime traffic scenes is curated from the BDD100K dataset for the evaluation of the proposed approach. The experiments demonstrate that, relative to the baseline, our method raises the mean average precision (${\mathrm{mAP}}_{50}$) from 51.5% to 56.6%, achieves a 7.3% decrease in parameter quantity, and maintains a fast inference speed of 208 FPS. For the challenging bike and motorbike categories, notable improvements in detection accuracy are achieved. Compared with other advanced YOLO-series models such as YOLOv11, the proposed model also exhibits significant performance advantages with a 3.7% higher ${\mathrm{mAP}}_{50}$. Furthermore, our model demonstrates good generalization performance on the larger BDD100K nighttime partition. The findings confirm that our approach significantly improves detection accuracy without compromising real-time processing, highlighting its potential as a lightweight vision module providing reliable perceptual inputs for autonomous vehicle control and safety actuators in challenging nighttime scenarios. 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

This review summarizes the research on the end effectors of agricultural harvesting robots (2010–2025) and extracts two core design principles. First of all, the selection of end effectors must follow the biological characteristics of fruits: rigid grippers are suitable for hard skinned and regular fruits; soft grippers can reduce the damage of fragile crops to a certain extent; suction cups are suitable for smooth, barrier free surfaces; the envelope type is suitable for soft and lossless picking scenes; the combined suction and grip design is more suitable for unstructured environments. Secondly, the separation mode should match the characteristics of the stem: motion separation (torsion/pull) is suitable for weak stems, while cutting is mainly used for hard stems. Unlike previous literature, this review provides a field deployability checklist (including dust/water proofing, cleanliness, maintenance, aging prevention, and aspiration prevention) to narrow the results of the laboratory and the real field environment. The three future directions of multimodal perception, variable stiffness driving and reinforcement learning are logically related to the analysis in this paper: multimodal perception optimizes the perception limit, variable stiffness solves the rigid–flexible trade-off, and reinforcement learning provides adaptive strategies for different crops. This framework can match the end effector design with the crop-specific field conditions. Full article

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes. Full article

Accurateestimation of clutter measurement density (CMD) is crucial for radar-based multi-target tracking (MTT), especially under spatially non-uniform and temporally varying environments. Existing methods, including finite mixture models, kernel density estimation, and normalizing flows, often require scene-specific tuning and exhibit limited generalization. To address these limitations, we propose HyperNCMD, a scene-adaptive CMD estimator that employs hypernetworks to dynamically generate the parameters of normalizing flows. To capture spatial variability, radar measurements are first embedded using Random Fourier Features (RFFs), and then processed by a spatio-temporal encoder that jointly models spatial structures and temporal clutter dynamics. The hypernetwork leverages the encoded embedding to adaptively produce flow parameters, enabling flexible CMD estimation across diverse environments. Lightweight data augmentation is further applied to make the estimator more robust across diverse environments, while a Feature-wise Linear Modulation (FiLM)-based fine-tuning scheme enhances test-time adaptation. Experiments on both synthetic and real radar datasets demonstrate that HyperNCMD achieves superior accuracy and robustness, achieving up to 10.5% reduction in per-point negative log-likelihood under dynamically varying conditions. These results highlight the potential of hypernetwork-driven CMD modeling for reliable radar perception in complex sensing environments. Full article

Under complex lighting conditions, particularly in low-light environments, general object detectors often suffer from degraded detection performance due to insufficient brightness, severe noise, and loss of discriminative details. This issue is especially critical in underground mining scenarios, where weak illumination, complex backgrounds, dust interference, and frequent small or partially occluded targets make reliable visual perception highly challenging. To address this issue, we propose an Illumination-Aware Detection Network (IADNet) for object detection in low-light environments. Specifically, an Illumination Modeling Subnetwork (IMS) is designed to extract illumination-aware and degradation-aware auxiliary features from low-light images. Within the IMS, an Adaptive Weighted Downsampling (AWD) layer is introduced to reduce noise interference during feature downsampling and enhance illumination-aware representation learning. Furthermore, a Global Feature Enhancement Module (GFEM) is incorporated to strengthen global context modeling and improve feature representation capability in complex scenes. In addition, an extra contrastive loss is introduced to constrain the optimization of the IMS, and weighting factors are employed to balance the detection loss and the contrastive loss during training. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method. On the public ExDark dataset, IADNet achieves an mAP@50 of 80.3%, outperforming the baseline YOLO11m by 3.4 percentage points. On the self-constructed mining low-light dataset Lowlight_Mine, the proposed method achieves 92.3% Precision, 82.0% Recall, 89.3% mAP@50, and 57.8% mAP@50:95, showing favorable performance in object detection tasks under mining-related low-light scenarios. On the DARK FACE dataset, IADNet achieves 54.6% mAP@50 and 31.2% mAP@50:95, further indicating its robustness under real low-light conditions. On the synthetic low-light Dark_VOC dataset, IADNet attains an mAP@50 of 91.6%, and on the normal-light VOC dataset, it achieves an mAP@50 of 93.0%, suggesting that the proposed method maintains stable detection performance under the evaluated illumination conditions. These results indicate that IADNet improves low-light object detection performance and provides a useful experimental reference for object detection tasks in mining-related low-light scenarios. Full article

The advancement of autonomous robotic systems has led to significant capabilities in perception, localization, mapping, and control, yet a critical challenge remains in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this challenge. The ONN formalizes relational semantic reasoning as a dynamic topological process by embedding Forman–Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, aiming to maintain relational integrity as scenes evolve. Building upon ONN, the ORTSF transforms reasoning traces into actionable control commands while compensating for system delays through predictive operators designed to preserve phase margins. Theoretical analysis and extensive simulations demonstrate that ORTSF maintains designed phase margins, offering advantages over classical delay compensation methods. Empirical studies indicate the framework’s effectiveness in unifying semantic cognition and robust control, providing a mathematically principled solution for cognitive robotics. Full article

To address the dual challenges of discriminating weeds from rice seedlings for precision weed management operations, such as targeted spraying and robotic weeding, in complex paddy-field scenes and deploying high-precision models on resource-limited edge devices, we propose DGS-Net, a deformable attention, GSConv-based feature fusion, and SEAM-enhanced lightweight network based on YOLOv11n. The backbone incorporates a convolutional block with parallel split attention and deformable attention transformer (C2PSA_DAT) module to improve the extraction of irregular and fine-grained weed features, the neck integrates a VoV-GSCSP module to enable lightweight multi-scale feature fusion for small and densely distributed targets, and a separated and enhancement attention module (SEAM) is placed before the detection head to enhance robustness under leaf occlusion and complex paddy-field background interference. In comparative experiments conducted on the paddy-field dataset under unified training and evaluation settings, DGS-Net achieved 91.7% precision, 86.8% recall, and 92.4% mean average precision (mAP), with a model size of 5.8 MB and a computational cost of 6.2 giga floating-point operations (GFLOPs). Compared with representative lightweight baseline detectors, DGS-Net showed a more favorable balance between detection accuracy and deployment efficiency. In additional edge-device deployment tests using the test set, the model sustained real-time inference at 32.5 FPS and achieved mAP@0.5, precision, and recall of approximately 0.928, 0.919, and 0.867, respectively. Overall, DGS-Net improves irregular feature extraction, enables lightweight multi-scale feature fusion, and increases robustness to occlusion while retaining strong deployability. The method therefore provides practical visual-perception support for precise, real-time crop–weed discrimination and precision weed management in complex paddy-field environments. Full article