Among all safety accidents in construction scenarios, collision accidents are regarded as one of the most common types of injury. To effectively prevent and monitor the occurrence of collision accidents, the computer graphics analysis technology has been used to assist collision detection and analysis; however, limitations remain in balancing the real-time performance with high precision of detection. To address this, a collision-detection method based on dynamic voxelization was proposed. This method integrated the generation of dynamic spatial voxel tree with the dynamic spherical voxelization calculation of resources to construct a collision detection and analysis mechanism. The core ideas are as follows: ① Based on the crowding-degree threshold, the space was recursively divided to generate a dynamic voxel tree, effectively filtering out non-collision risk areas. ② The side length of voxel units were dynamically calculated according to the relative distance between resources and resource volume, realizing the adaptive adjustment of voxel granularity. ③ Spherical voxels were used instead of traditional cubic voxels to avoid the computational burden of non-axis-aligned detection. ④ A hollowing-out procedure was introduced to eliminate internal invalid voxels, further optimizing detection efficiency. This method can accurately capture resource interactions in complex dynamic construction environments, significantly improving detection accuracy and optimizing computational efficiency. Experimental results showed that compared with traditional methods, the proposed method significantly improved the detection accuracy, with precision and accuracy reaching 94.64% and 96.67%, respectively. In terms of collision detection time, it was more efficient than most existing methods, with a calculation speed increase of at least about 11.36%. At the same time, the study analyzed the impact of key parameters such as voxel-tree depth, root-node size, and voxel side length on performance, and analyzed the consumption of CPU resources and memory resources by the method in scenarios of different scales. The consumption was within an acceptable range, verifying the applicability of the method in construction scenarios. The method provided an effective new idea of information processing for enhancing the intelligent level of construction safety management.

To address the challenges in integrating Digital-Twin (DT) technology with large-scale generative models in industrial design, a CAD-LDT digital-twin modeling framework based on generative foundation models was proposed. The framework adopted a triadic architecture consisting of a physical-entity module, an intelligent generation module, and a virtual-entity module, and innovatively incorporated multi-modal data fusion mechanisms and domain-knowledge constraints to enable autonomous generation of parameterized CAD models from physical-entity descriptions. Utilizing LLaVA-7B and LLaMA-7B as backbone models, the framework employed LoRA-based lightweight adapters to achieve cross-modal alignment between visual and textual features, and introduced a constraint encoder that transformed geometric tolerances and physical rules into structured JSON objects. To enhance the mathematical consistency of spatial transformations, Lie-group algorithms were adopted for the optimization of rigid-body transformations, while a geometric-weight binning strategy was proposed to discretize complex assembly relationships. Moreover, a spatiotemporal-decoupled generation strategy was designed to jointly optimize spatial layout and assembly sequencing. Experimental results on the DeepCAD dataset indicated that the proposed framework achieved an Intersection- over-Union (IoU) of 83.6%, a constraint satisfaction rate of 91.3%, and a 26.5% improvement in generation efficiency, significantly outperforming existing baseline models. Further ablation studies confirmed the critical contributions of multi-modal fusion, constraint encoding mechanisms, and Lie-group optimization to modeling performance, providing a novel DT modeling paradigm for intelligent manufacturing with demonstrated value in parametric design and assembly process optimization.

To address the issues of high computational cost and complex network models in point cloud classification and segmentation methods, a point cloud classification and segmentation algorithm based on lightweight networks and weighted Random Forest (RF) was proposed. The algorithm achieved efficient classification and segmentation in a hierarchical manner. Firstly, to address the issues of multiple layers and complex computation in traditional neural networks, a lightweight neural network was constructed to extract point cloud features such as global shape, inter-regional relationships, curvature, normal vector, and color, thereby achieving rapid rough classification and segmentation of point clouds. Then, to address data imbalance, an adaptive classification and segmentation strategy was designed. By introducing a weighted RF and combining inconsistency-measurement screening with dynamic-weighting optimization mechanisms, fine classification and segmentation of point clouds were achieved. The algorithm conducted classification experiments on the ModelNet40 dataset and segmentation experiments on the Semantic3D dataset and outdoor-scene point-cloud data. The results showed that compared with Local Geo-Transformer, PointNeXt, and FastPointNet++, classification and segmentation accuracy increased by approximately 1.9%, 1.6%, and 1.7%, respectively, while classification and segmentation time was reduced by approximately 40%, 30%, and 20%, respectively. Thus, the proposed point-cloud classification and segmentation algorithm based on lightweight networks and weighted RF can effectively reduce the training time of the model and improve the efficiency of classification and segmentation while maintaining high accuracy, making it an effective point cloud classification and segmentation algorithm.

The detection of Focal Liver Lesions (FLLs) is crucial for disease diagnosis and treatment. Traditional detection methods face many challenges, and the application of deep-learning technology brings new opportunities. In view of this, this paper systematically reviewed the deep-learning-based FLLs detection methods, and provided specific research directions for the development of FLLs detection technology by analyzing the advantages and disadvantages of related technologies. First, the public datasets of liver radiological images were organized and summarized, and the key role of data preprocessing in improving model performance was expounded. Secondly, the 2D and 3D detection algorithms based on convolutional neural networks, Transformer, knowledge distillation, and other technologies were compared and analyzed, revealing the technical evolution path from local feature modeling to global spatio-temporal correlation. In addition, the temporal feature fusion methods for multi-phase images were examined in depth, providing new ideas for dynamic lesion characterization. The review showed that existing methods had achieved breakthroughs in detection accuracy and efficiency, but still faced challenges such as insufficient sensitivity to small lesions, weak cross-device generalization, and lack of clinical verification. Future research was recommended to accelerate the clinical transformation and application of deep learning in auxiliary diagnosis of liver lesions through multi-center data collaboration, lightweight algorithm design, and enhanced interpretability.

The traditional development approach for complex forming equipment typically relies on Document-Based Systems Engineering (DBSE), which often leads to issues such as protracted development cycles due to inadequate requirement analysis, incomplete requirement coverage caused by textual ambiguity, and equipment development delays lagging behind technological iterations. These shortcomings frequently result in final designs that fail to meet target performance metrics and require inefficient, repetitive modifications. Therefore, in the conceptual design stage of complex forming equipment, and drawing on the U.S. Department of Defense Architecture Framework (DoDAF) combined with Model-Based Systems Engineering (MBSE), an MBSE-based conceptual-design method for complex forming equipment was proposed. This method utilized five viewpoints, including panoramic viewpoint, capability viewpoint, operational viewpoint, systems viewpoint, and standards viewpoint, as entry points for the conceptual design of complex forming equipment. Through multi-perspective analysis, the method performed top-level requirements acquisition, requirements refinement analysis, functional analysis, and system modeling across four design levels. Eleven types of models were established using the Systems Modeling Language (SysML), enabling digital and procedural expression in the conceptual design stage of complex forming equipment. Finally, superplastic-forming equipment was used as a representative example to demonstrate the application of this design method. The application of the method addressed the shortcomings of traditional design approaches and demonstrated that the method provided effective guidance for the forward development of complex forming equipment.

With the rapid development of artificial intelligence technology, multimodal robots are playing an increasingly important role in preschool children’s education, entertainment, and daily life. Existing studies have primarily focused on the effects of single sensory cues of robots on children’s perception, while systematic research on multisensory integration effects remains limited. To explore how robots’ multimodal features jointly influence children’s emotional preferences and visual attention, 318 children aged 4-6 years were recruited to participate in an eye-tracking experiment. The experiment adopted a 2 (appearance features: humanoid vs. animal-like) × 3 (voice guidance: male voice, female voice, none) × 2 (gesture guidance: present vs. absent) mixed factorial design, with robot appearance features (humanoid vs. animal-like) and behavioral features (voice and gesture guidance) as independent variables, and children’s emotional preferences and eye-tracking indicators as dependent variables, thereby systematically examining the effects of multimodal features on child users. The results showed that, in terms of appearance features, no significant difference was observed in subjective preference ratings between humanoid and animal-like robots. However, humanoid robots attracted longer total fixation duration, more fixation counts, and shorter first-fixation latency, indicating superior attention-related performance compared with animal-like robots. Children were more readily attracted to humanoid robots during the initial stage of visual contact, and anthropomorphic design showed greater advantages in sustaining children’s attention. In terms of behavioral features, robots with gesture guidance received significantly higher subjective preference ratings than those without gestures, and also elicited longer total fixation duration and more fixation counts. Robots with female voices received slightly higher subjective preference ratings than those with male voices, and both were significantly preferred over robots without voices. Robots with male voices had slightly longer total fixation duration than those with female voices, and both significantly outperformed robots without voices. The difference in fixation counts between male- and female-voice robots was not significant, but both attracted significantly more fixations than robots without voices. Robots with gesture guidance and voice (especially female voice) performed better in subjective ratings and visual attention allocation, suggesting that behavioral features substantially enhanced children’s emotional preferences and interactive experiences. Furthermore, the effects of appearance and behavioral features on children’s emotional preferences and visual attention were relatively independent, and no significant interaction effects were observed. This study revealed the mechanisms through which robot appearance and behavioral features influenced preschool children’s emotional preferences and visual attention, thereby providing scientific evidence for designing child-oriented robots that align with users’ emotional needs.

To address the significant decline in matching accuracy and the number of correspondences exhibited by existing image-matching methods under large viewpoint variations, an improved image-matching approach based on E-LoFTR was proposed. Firstly, based on a strategy of viewpoint rectification followed by fine-grained matching, a novel two-stage SIFT-based viewpoint-rectification module was proposed, which leveraged the viewpoint invariance of the Scale-Invariant Feature Transform (SIFT) algorithm and the geometric alignment capability of homography to enhance matching accuracy under large viewpoint variations. Then, a directional-gated attention mechanism was designed that employed a cascaded structure of multi-directional convolutions and dynamic gating to extract queries (Q), keys (K), and values (V). The injected geometric priors significantly enhanced the model’s robustness. Lastly, to mitigate information loss during the upsampling of fused features, the Fusion-DySample module was incorporated to further improve performance. Experimental results on the public MegaDepth dataset showed that our method achieved relative pose estimation AUCs of 57.1%, 72.7%, and 83.9% under rotation error thresholds of 5°, 10°, and 20°, respectively, outperforming E-LoFTR by 0.7%, 0.5%, and 0.4%. On the newly constructed NewMega dataset based on MegaDepth and on a private industrial dataset, our method also demonstrated substantial improvements in both the number of matches and matching accuracy.

In intelligent traffic monitoring systems, small target vehicle detection in complex scenes faces challenges such as low feature resolution, severe occlusion interference, computational redundancy, and insufficient bounding-box regression accuracy. To balance detection accuracy with deployment efficiency on edge devices, an improved YOLOv8 framework based on dynamic sparse attention and a lightweight dual-branch structure was proposed. The method first introduced a bidirectional routing sparse attention mechanism (ReBiAttention) that enhanced the retention of shallow features for small targets by dynamically filtering key features through a two-level routing strategy. Subsequently, GSConv and VoV-GSCSP modules were integrated to reduce computational cost while dynamically adjusting multi-scale feature weights. An improved DynamicHead was applied for multi-task adaptive optimization, and a modified ShapeIoU loss function with shape- and scale-aware weighting was employed to improve localization accuracy. Experiments on the UA-DETRAC dataset showed that, relative to baseline YOLOv8n, Precision, Recall, and mAP@0.5 increased by 8.739%, 1.685%, and 7.225%, respectively, while the parameter count decreased by 4.3%. This method provided an efficient solution for accurate detection of small-target vehicles in complex traffic scenarios.

Aeroengine blades are core components of engines; tiny surface defects can lead to serious safety accidents. Traditional vision detection technology is limited by motion blur, low dynamic range, background redundancy, and so forth. To address these challenges, a method of aeroengine blade defect detection based on Dynamic Vision Sensor (DVS) was proposed. Dynamic vision sensor produced data in an asynchronous event-stream format, and were therefore referred to as event camera, which exhibited the advantages of large dynamic range, high frame rate, and strong ability to capture small targets. Firstly, a defect detection platform based on DVS was built, and its imaging characteristics and advantages were explored. On this basis, the first Event-based Defect Detection Dataset of Aeroengine Blade (EDD-AB) dataset based on DVS was constructed, covering nearly 6 000 images of scratches, point marks and edge damage, with approximately 12 000 finely annotated target labels. The dataset was released as open source (link: https://github. com/NiBieZhouMei5520/EDD-AB.git). Furthermore, a multi-scale defect-detection algorithm based on asynchronous event-stream frame aggregation (AEAF-ABDD) was proposed: event streams were visualized through frame aggregation technology using a fixed time window; a Multi-Resolution Adaptive Feature Pyramid Network (MRAFPN) was developed to enhance multi-scale defect feature extraction capability; a lightweight SimAM attention mechanism was incorporated to strengthen focus on key regions; a star-convolution module (StarNet) was fused to improve the efficiency of high-dimensional nonlinear feature mapping, enabling accurate detection of multi-scale defects on complex curved workpieces. Experiments demonstrated that AEAF-ABDD achieved a mean Average Precision (mAP) of 97.7% on the EDD-AB dataset and a detection speed of 105 frames per second, substantially outperforming mainstream algorithms. An efficient solution for automated quality inspection of highly reflective curved workpieces was thereby provided, promoting the application of DVS in the field of industrial inspection.

With the widespread application of Building Information Modeling (BIM) technology in the field of engineering design, the Revit platform-driven 3D forward design has developed into a relatively mature solution for superstructure. However, as a critical structural component, isolated foundations still face challenges such as low efficiency and poor information integrity in 3D modeling and drawing generation. Moreover, due to the closed data interfaces of mainstream structural analysis software, it is extremely challenging to directly extract reinforcement information from fundamental database files and synchronize to the properties of Revit elements. To address this limitation, a new parametric modeling algorithm for isolated foundations based on AutoCAD layer and text recognition was proposed, utilizing the Revit Application Programming Interface (API) and the Model-View-ViewModel (MVVM) design pattern. By importing foundation layout and reinforcement annotation drawings generated by structural analysis software (e.g., YJK) into Revit, the algorithm calculated the center coordinates and planar dimensions of foundations using layer-recognition methods. Then, a matching logic was established between foundations and reinforcement annotations according to their relative positions. Subsequently, text recognition was employed to extract the corresponding annotations’ numbering, height, and reinforcement information, which were then automatically written into the family properties of the model. Additionally, the extraction of model attributes and geometric face references enabled automatic annotation of foundation dimensions and reinforcement. Finally, the proposed method was applied to the parametric modeling and drafting of isolated foundations for a self- built cold storage factory and was compared against conventional methods. The results demonstrated that this algorithm significantly improved the efficiency of three-dimensional modeling and annotation drawing for isolated foundations, while exhibiting excellent compatibility with calculation files exported from PKPM software.