Search Results (4,823)

Environmental perception is crucial for safe autonomous driving, enabling accurate analysis of the vehicle’s surroundings. While 3D LiDAR is traditionally used for 3D environment reconstruction, its high cost and complexity present challenges. In contrast, camera-based cross-view frameworks can offer a cost-effective alternative. Hence, this manuscript proposes a new cross-view model to extract mapping features from camera images and then transfer them to a Bird’s-Eye View (BEV) map. Particularly, a multi-head attention mechanism in the decoder architecture generates the final semantic map. Each camera learns embedding information corresponding to its position and angle within the BEV map. Cross-view attention fuses information from different perspectives to predict top-down map features enriched with spatial information. The multi-head attention mechanism then globally performs dependency matches, enhancing long-range information and capturing latent relationships between features. Transposed convolution replaces traditional upsampling methods, avoiding high similarities of local features and facilitating semantic segmentation inference of the BEV map. Finally, we conduct numerous simulation experiments to verify the performance of our cross-view model. Full article

Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects. The characteristics of carbon traces, such as multiple sizes, diverse morphologies, and irregular edges, pose severe challenges for segmentation accuracy and inference speed. In this paper, a feasible real-time network (deformable-spatial-Canny segmentation network, DSC-SeNet) was designed for carbon trace segmentation. To improve inference speed, a lightweight unilateral feature extraction framework is constructed based on a shallow feature sharing mechanism, which is designed to provide feature input for both semantic path and spatial path. Meanwhile, the segmentation model is improved in two aspects for better segmentation accuracy. For one aspect, to better perceive diverse morphology and edge features of carbon trace, three measures, including deformable convolution (DFC), Canny edge operator, and spatial feature refinement module (SFRM), were adopted for feature perception, enhancement, and aggregation, respectively. For the other aspect, to improve the fusion of semantic features and spatial features, coordinate attention feature aggregation (CAFA) is designed to reduce feature aggregation loss. Experimental results showed that the proposed DSC-SeNet outperformed state-of-the-art models with a good balance between segmentation accuracy and inference speed. For a 512 × 512 input, it achieved 84.7% mIoU, which is 6.4 percentage points higher than that of the baseline short-term dense convolution network (STDC), with a speed of 94.3 FPS on an NVIDIA GTX 2050Ti. This study provides technical support for real-time segmentation of carbon traces and transformer insulation assessment. Full article

In addition to their practical and aesthetic qualities, ceramic teapots are highly decorative and stylish. Based on the theory of perceptual engineering, this study employs eye-tracking technology and semantic-difference methods to investigate user preferences for ceramic teapot shapes. Using eye-movement experiments, the study first determines users’ visual attention to different morphological regions. Using the orthogonal alignment method, nine styling samples were developed by combining expert classification of classic and traditional teapot styling elements. By combining a semantic perception questionnaire with a satisfaction questionnaire, the study evaluated users’ visual attention to these samples and their satisfaction with them. It was found that shapes characterized by classic, rounded, and proportional coordination were more in line with consumers’ aesthetic preferences despite the differences in semantic evaluations among consumers of different genders and ages, which led to relatively consistent consumer satisfaction. The purpose of this study is not only to provide a scientific basis for styling ceramic teapots but also to assist designers in grasping the laws of consumer preference in order to create better products. Full article

As an interdisciplinary field of natural language processing and computer vision, Visual Question Answering (VQA) has emerged as a prominent research focus in artificial intelligence. The core of the VQA task is to combine natural language understanding and image analysis to infer answers by extracting meaningful features from textual and visual inputs. However, most current models struggle to fully capture the deep semantic relationships between images and text owing to their limited capacity to comprehend feature interactions, which constrains their performance. To address these challenges, this paper proposes an innovative Trilinear Multigranularity and Multimodal Adaptive Fusion algorithm (TriMMF) that is designed to improve the efficiency of multimodal feature extraction and fusion in VQA tasks. Specifically, the TriMMF consists of three key modules: (1) an Answer Generation Module, which generates candidate answers by extracting fused features and leveraging question features to focus on critical regions within the image; (2) a Fine-grained and Coarse-grained Interaction Module, which achieves multimodal interaction between question and image features at different granularities and incorporates implicit answer information to capture complex multimodal correlations; and (3) an Adaptive Weight Fusion Module, which selectively integrates coarse-grained and fine-grained interaction features based on task requirements, thereby enhancing the model’s robustness and generalization capability. Experimental results demonstrate that the proposed TriMMF significantly outperforms existing methods on the VQA v1.0 and VQA v2.0 datasets, achieving state-of-the-art performance in question–answer accuracy. These findings indicate that the TriMMF effectively captures the deep semantic associations between images and text. The proposed approach provides new insights into multimodal interaction and fusion research, combining domain adaptation techniques to address a broader range of cross-domain visual question answering tasks. Full article

Background: Traditional methods for analysing surgical processes often fall short in capturing the intricate interconnectedness between clinical procedures, their execution sequences, and associated resources such as hospital infrastructure, staff, and protocols. Aim: This study addresses this gap by developing an ontology for appendicectomy, a computational model that comprehensively represents appendicectomy processes and their resource dependencies to support informed decision making and optimise appendicectomy healthcare delivery. Methods: The ontology was developed using the NeON methodology, drawing knowledge from existing ontologies, scholarly literature, and de-identified patient data from local hospitals. Results: The resulting ontology comprises 108 classes, including 11 top-level classes and 96 subclasses organised across five hierarchical levels. The 11 top-level classes include “clinical procedure”, “appendicectomy-related organisational protocols”, “disease”, “start time”, “end time”, “duration”, “appendicectomy outcomes”, “hospital infrastructure”, “hospital staff”, “patient”, and “patient demographics”. Additionally, the ontology includes 77 object and data properties to define relationships and attributes. The ontology offers a semantic, computable framework for encoding appendicectomy-specific clinical procedures and their associated resources. Conclusion: By systematically representing this knowledge, this study establishes a foundation for enhancing clinical decision making, improving data integration, and ultimately advancing patient care. Future research can leverage this ontology to optimise healthcare workflows and outcomes in appendicectomy management. Full article

Salient object detection in optical remote sensing images (ORSI-SOD) encounters notable challenges, mainly because of the small scale of salient objects and the similarity between these objects and their backgrounds in images captured by satellite and aerial sensors. Conventional approaches frequently struggle to efficiently leverage multi-scale and multi-stage features. Moreover, these methods usually rely on sophisticated and resource-heavy architectures, which can limit their practicality and efficiency in real-world applications. To overcome these limitations, this paper proposes a novel lightweight network called the Multi-scale Feature Fusion Network (MFFNet). Specifically, a Multi-stage Information Fusion (MIF) module is created to improve the detection of salient objects by effectively integrating features from multiple stages and scales. Additionally, we design a Semantic Guidance Fusion (SGF) module to specifically alleviate the problem of semantic dilution often observed in U-Net architecture. Comprehensive evaluations on two benchmark datasets show that the MFFNet attains outstanding performance in four out of eight evaluation metrics while only having 12.14M parameters and 2.75G FLOPs. These results highlight significant advancements over 31 state-of-the-art models, underscoring the efficiency of MFFNet in salient object-detection tasks. Full article

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters. Full article

As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available. Full article

As power system equipment gradually ages, the automated disassembly of transformers has become a critical area of research to enhance both efficiency and safety. This paper presents a transformer disassembly system designed for power systems, leveraging multimodal perception and collaborative processing. By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. The system employs an enhanced YOLOv8 model for positioning and identifying screw-fastened covers while also utilizing the STDC network for segmentation and cutting path planning of welded covers. In addition, the system captures 3D point cloud data of the transformer’s interior using multi-view RGB-D cameras and performs multimodal semantic segmentation and object detection via the ODIN model, facilitating the high-precision identification and cutting of complex components such as windings, studs, and silicon steel sheets. Experimental results show that the system achieves a recognition accuracy of 99% for both cover and internal component disassembly, with a disassembly success rate of 98%, demonstrating its high adaptability and safety in complex industrial environments. Full article

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. Full article

The COVID-19 pandemic caused unprecedented global disruptions, with the hotel and aviation industries—two critical pillars of tourism—among the hardest hit. This study analyzed 451 hotel-related and 336 aviation-related records from the Web of Science database, applying semantic network analysis to uncover eight clusters of crisis management knowledge: basic functions, crisis response, operational strategies, epidemic prevention and control, crisis perception, innovative services, scope of influence, and internal and external environments. Latent Dirichlet Allocation (LDA) topic modeling identified distinct thematic strategies for each sector. In hotels, these included Digital Innovation Transformation, Monitoring Management Procedures, Emotional Awareness Incentives, and Resilience Mechanism Establishment. In aviation, strategies focused on Green Economic Transformation, Co-creation Value Realization, Passenger Incentive Mechanisms, and Balancing Health Risks. By visualizing co-occurrence relationships and mapping thematic intersections and divergences, this study provides actionable insights into the recovery strategies of these industries. The findings offer robust support for developing targeted management approaches and decision-making frameworks to ensure the sustainable growth of the tourism sector. Full article

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek language and, given the scientifically certified usability of the ontological structuring of data in the field of the semantic web and cognitive computing, a new ontology of the Modern Greek language at the level of structure and vocabulary is presented in this paper, using the Protégé platform. With the specific logical and structured form of knowledge representation to express, this research processes and exploits in an easy and useful way the distributed semantics of linguistic information. Full article

Geographic knowledge graph representation learning embeds entities and relationships in geographic knowledge graphs into a low-dimensional continuous vector space, which serves as a basic method that bridges geographic knowledge graphs and geographic applications. Previous geographic knowledge graph representation methods primarily learn the vectors of entities and their relationships from their spatial attributes and relationships, which ignores various semantics of entities, resulting in poor embeddings on geographic knowledge graphs. This study proposes a two-stage multimodal geographic knowledge graph representation (MGKGR) model that integrates multiple kinds of semantics to improve the embedding learning of geographic knowledge graph representation. Specifically, in the first stage, a spatial feature fusion method for modality enhancement is proposed to combine the structural features of geographic knowledge graphs with two modal semantic features. In the second stage, a multi-level modality feature fusion method is proposed to integrate heterogeneous features from different modalities. By fusing the semantics of text and images, the performance of geographic knowledge graph representation is improved, providing accurate representations for downstream geographic intelligence tasks. Extensive experiments on two datasets show that the proposed MGKGR model outperforms the baselines. Moreover, the results demonstrate that integrating textual and image data into geographic knowledge graphs can effectively enhance the model’s performance. Full article

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved. Full article

The quality of silkworm cocoons affects the quality and cost of silk processing. It is necessary to sort silkworm cocoons prior to silk production. Cocoon images consist of fine-grained images with large intra-class differences and small inter-class differences. The subtle intra-class features pose a serious challenge in accurately locating the effective areas and classifying silkworm cocoons. To improve the perception of intra-class features and the classification accuracy, this paper proposes a bilinear pooling classification model (B-Res41-ASE) based on adaptive multi-scale feature fusion and enhancement. B-Res41-ASE consists of three parts: a feature extraction module, a feature fusion module, and a feature enhancement module. Firstly, the backbone network, ResNet41, is constructed based on the bilinear pooling algorithm to extract complete cocoon features. Secondly, the adaptive spatial feature fusion module (ASFF) is introduced to fuse different semantic information to solve the problem of fine-grained information loss in the process of feature extraction. Finally, the squeeze and excitation module (SE) is used to suppress redundant information, enhance the weight of distinguishable regions, and reduce classification bias. Compared with the widely used classification network, the proposed model achieves the highest classification performance in the test set, with accuracy of 97.0% and an F1-score of 97.5%. The accuracy of B-Res41-ASE is 3.1% and 2.6% higher than that of the classification networks AlexNet and GoogLeNet, respectively, while the F1-score is 2.5% and 2.2% higher, respectively. Additionally, the accuracy of B-Res41-ASE is 1.9% and 7.7% higher than that of the Bilinear CNN and HBP, respectively, while the F1-score is 1.6% and 5.7% higher. The experimental results show that the proposed classification model without complex labelling outperforms other cocoon classification algorithms in terms of classification accuracy and robustness, providing a theoretical basis for the intelligent sorting of silkworm cocoons. Full article