Search Results (5,717)

With the rise of embodied agents and indoor service robots, object detection has become a critical component supporting semantic mapping, path planning, and human–robot interaction. However, indoor scenes often face challenges such as severe occlusion, large-scale variations, small and densely packed objects, and complex textures, making existing methods struggle in terms of both robustness and accuracy. This paper proposes MDF-YOLO, a multi-domain fusion detection framework based on Hölder regularity guidance. In the backbone, neck, and feature recovery stages, the framework introduces the CrossGrid Memory Block, Hölder-Based Regularity Guidance–Hierarchical Context Aggregation module, and Frequency-Guided Residual Block, achieving complementary feature modeling across the state space, spatial domain, and frequency domain. In particular, the HG-HCA module uses the Hölder regularity map as a guiding signal to balance the dynamic equilibrium between the macro and micro paths, thus achieving adaptive coordination between global consistency and local discriminability. Experimental results show that MDF-YOLO significantly outperforms mainstream detectors in metrics such as mAP@0.5, mAP@0.75, and mAP@0.5:0.95, achieving values of 0.7158, 0.6117, and 0.5814, respectively, while maintaining near real-time inference efficiency in terms of FPS and latency. Ablation studies further validate the independent and synergistic contributions of CGMB, HG-HCA, and FGRB in improving small-object detection, occlusion handling, and cross-scale robustness. This study demonstrates the potential of Hölder regularity and multi-domain fusion modeling in object detection, offering new insights for efficient visual modeling in complex indoor environments. Full article

Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can lead to negative effects on high-level computer vision tasks, such as object detection, recognition and tracking, etc. To avoid the negative influence of low-quality maritime images, it is necessary to develop a visual perception enhancement method for intelligent surface vehicles. To generate satisfactory haze-free maritime images, we propose development of a novel transmission map estimation and refinement framework. In this work, the coarse transmission map is obtained by the weighted fusion of transmission maps generated by dark channel prior (DCP)- and luminance-based estimation methods. To refine the transmission map, we take the segmented smooth feature of the transmission map into account. A joint variational framework with total generalized variation (TGV) and relative total variation (RTV) regularizers is accordingly proposed. The joint variational framework is effectively solved by an alternating-direction numerical algorithm, which decomposes the original nonconvex nonsmooth optimization problem into several subproblems. Each subproblem could be efficiently and easily handled using the existing optimization algorithm. Finally, comprehensive experiments are conducted on synthetic and realistic maritime images. The imaging results have illustrated that our method can outperform or achieve comparable results with other competing dehazing methods. The promoted visual perception is beneficial to improve navigation safety for intelligent surface vehicles under hazy weather conditions. Full article

Critical details in both bright and dark regions are frequently lost in high dynamic range (HDR) images when they are displayed on low dynamic range (LDR) devices. To mitigate this issue, tone mapping operators (TMOs) have been developed to convert HDR images into LDR representations while maintaining perceptual quality. However, it is challenging to effectively balance various key visual attributes, such as naturalness and structural fidelity. To overcome this limitation, a two-stage Bayesian optimization approach was proposed in this work to enhance the perceptual quality of tone-mapped images across multiple evaluation metrics. The first stage adaptively optimizes TMQI parameters to capture image-specific perceptual characteristics, while the second stage refines the tone mapping function to further improve detail preservation and visual realism. Extensive experiments using three distinct HDR benchmark datasets were conducted, indicating that the proposed method generally performs better than the existing tone mapping techniques across most evaluated metrics, including TMQI, Naturalness, and Structural Fidelity. Our adaptive approach offers a robust and effective solution for optimizing HDR image conversion, resulting in a significantly improved perceptual quality compared to traditional methods. Full article

Renewable energy integration is no longer a solely technical endeavor; it necessitates a multidimensional transformation that spans technological, economic, environmental, social, and regulatory dimensions. This review presents a visual and strategic framework for addressing the complex challenges of integrating solar, wind, hydro, geothermal, and biomass energy systems. The objective is to redefine traditional approaches by linking specific integration barriers to tailored strategies and measurable outcomes. The study uses comparative analysis, regional case studies, and a variety of visual tools—such as flowcharts, spider charts, and challenge–strategy–outcome maps—to spatially express interdependencies and trade-offs. These tools enable stakeholders to determine the best integration pathways based on performance measures, regional restrictions, and system synergies. The results reveal that visual mapping not only clarifies complex system dynamics, but also enhances stakeholder collaboration by translating technical data into accessible formats. The framework supports adaptive planning, smart grid adoption, and community-centered microgrid development. In conclusion, the study provides a forward-looking strategy for developing resilient, inclusive, and intelligent renewable energy systems. It highlights that future energy resilience will be built on integrated, regionally informed, and socially inclusive design, with technology, policy, and community engagement combined to drive sustainable energy transitions. Full article

Spasticity of the distal lower limb substantially impairs stance, gait, and quality of life in patients with upper motor neuron lesions. Although ultrasound-guided botulinum toxin A (BoNT-A) injections are increasingly employed, structured, muscle-specific visual guidance for the distal lower limb remains limited. This study provides a comprehensive guide for ultrasound-guided BoNT-A injections across ten key distal lower limb muscles: gastrocnemius, soleus, tibialis posterior, flexor hallucis longus, flexor digitorum longus, tibialis anterior, extensor hallucis longus, flexor digitorum brevis, flexor hallucis brevis, and extensor digitorum longus. For each muscle, we present (1) Anatomical positioning relative to osseous landmarks; (2) Sonographic identification cues and dynamic features; (3) Zones of intramuscular neural arborization optimal for injection; (4) Practical injection protocols derived from literature and clinical experience. High-resolution ultrasound images and dynamic videos illustrate real-life muscle behavior and guide injection site selection. This guide facilitates precise targeting by correlating sonographic signs with optimal injection zones, addresses common spastic patterns—including equinus, varus, claw toe, and hallux deformities—and integrates fascial anatomy with motor-point mapping. This article completes the Elias University Hospital visual series, providing clinicians with a unified framework for effective spasticity management to improve gait, posture, and patient autonomy. Full article

Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A machine vision inspection method based on a two-stage fusion network is proposed in this paper, aiming to achieve accurate cutting of salmon parts and efficient recognition of defects. The fish body image is collected by building a visual inspection system, and the dataset is constructed by preprocessing and data enhancement. For the part cutting, the improved U-Net model that introduces the CBAM attention mechanism is used to strengthen the extraction ability of the fish body texture features. For defect detection, the two-stage fusion architecture is designed to quickly locate the defective region by adding the YOLOv5 of the P2 small target detection layer first, and then the cropped region is fed into the improved U-Net for accurate cutting. The experimental results demonstrate that the improved U-Net achieves a mean average precision (mAP) of 96.87% and a mean intersection over union (mIoU) of 94.33% in part cutting, representing improvements of 2.44% and 1.06%, respectively, over the base model. In defect detection, the fusion model attains an mAP of 94.28% with a processing speed of 7.30 fps, outperforming the single U-Net by 28.02% in accuracy and 236.4% in efficiency. This method provides a high-precision, high-efficiency solution for intelligent salmon processing, offering significant value for advancing automation in the aquatic product processing industry. Full article

In this study, we propose an interactive environment-aware dialog and planning system for social robots in early childhood education, aimed at supporting the learning and social interaction of young children. The proposed architecture consists of three core modules. First, semantic simultaneous localization and mapping (SLAM) accurately perceives the environment by constructing a semantic scene representation that includes attributes such as position, size, color, purpose, and material of objects, as well as their positional relationships. Second, the automated planning system enables stable task execution even in changing environments through planning domain definition language (PDDL)-based planning and replanning capabilities. Third, the visual question answering module leverages scene graphs and SPARQL conversion of natural language queries to answer children’s questions and engage in context-based conversations. The experiment conducted in a real kindergarten classroom with children aged 6 to 7 years validated the accuracy of object recognition and attribute extraction for semantic SLAM, the task success rate of the automated planning system, and the natural language question answering performance of the visual question answering (VQA) module.The experimental results confirmed the proposed system’s potential to support natural social interaction with children and its applicability as an educational tool. Full article

Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, and hallways. Using mobile eye tracking with concurrent think-aloud protocols and schematic mapping, we examined visual attention patterns during predefined navigation tasks performed by 24 first-time visitors. Findings revealed frequent fixations on non-informative structural features, while existing wayfinding cues were often overlooked. High rates of null gazes indicated unsuccessful visual searching. Thematic analysis of verbal data identified eight key issues, including spatial confusion, aesthetic monotony, and inadequate signage. Participants frequently described the environment as disorienting and emotionally taxing, comparing it to institutional settings such as hospitals. In response, we developed wayfinding design proposals informed by our research findings, stakeholder needs, and contextual priorities. We used an experiential digital twin that prioritized perceptual fidelity to analyze the current wayfinding challenges, develop experimental protocols, and discuss design options and costs. This study offers a transferable methodological framework for identifying wayfinding challenges through convergent analysis of gaze patterns and verbal protocols, demonstrating how empirical findings can inform targeted wayfinding design interventions. Full article

Accurate diagnosis of myocardial infarction (MI) holds significant clinical importance for public health systems. Deep learning-based ECG, classification and localization methods can automatically extract features, thereby overcoming the dependence on manual feature extraction in traditional methods. However, these methods still face challenges such as insufficient utilization of dynamic information in cardiac cycles, inadequate ability to capture both global and local features, and data imbalance. To address these issues, this paper proposes a ResNet-Transformer cascaded network (RTCN) to process time frequency features of ECG signals generated by the S-transform. First, the S-transform is applied to adaptively extract global time frequency features from the time frequency domain of ECG signals. Its scalable Gaussian window and high phase resolution can effectively capture the dynamic changes in cardiac cycles that traditional methods often fail to extract. Then, we develop an architecture that combines the Transformer attention mechanism with ResNet to extract multi-scale local features and global temporal dependencies collaboratively. This compensates for the existing deep learning models’ insufficient ability to capture both global and local features simultaneously. To address the data imbalance problem, the Denoising Diffusion Probabilistic Model (DDPM) is applied to synthesize high-quality ECG samples for minority classes, increasing the inter-patient accuracy from 61.66% to 68.39%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that the model’s attention areas are highly consistent with pathological features, verifying its clinical interpretability. Full article

Accurate apple orchard mapping plays a vital role in managing agricultural resources. However, national-scale apple orchard mapping faces challenges such as the “same spectrum with different objects” phenomenon between apple trees and other crops, as well as difficulties in sample collection. To address the above issues, this study proposes a knowledge-assisted apple mapping framework that automatically generates samples using agronomic knowledge and employs a random forest algorithm for classification. Firstly, an apple mapping composite index (AMCI) was developed by integrating the chlorophyll content and leaf structural characteristics of apple trees. In a single Sentinel-2 image, a novel natural vegetation phenolic compounds index was applied to systematically exclude natural vegetation, and based on this, the AMCI was used to generate an initial apple distribution map. Using this initial map, apple samples were obtained through random point selection and visual interpretation, and other samples were constructed based on land cover products. Finally, a 10 m-resolution apple orchard map of China was generated with the random forest algorithm. The results show an overall accuracy of 90.7% and a kappa of 0.814. Moreover, the extracted area shows an 82.11% consistency with official statistical data, demonstrating the effectiveness of the proposed method. This simple and robust framework provides a valuable reference for large-scale crop mapping. Full article

The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners. Full article

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterprets spectrograms as visual objects and applies Feature Coding for Machines (FCM) to anomalous sound detection (ASD). In our approach, audio signals are transformed log-mel spectrograms, from which intermediate feature maps are extracted, compressed, and reconstructed through the FCM pipeline. For comparison, we implement AAC-LC (Advanced Audio Coding Low Complexity) as a representative perceptual audio codec and VVC (Versatile Video Coding) as spectrogram-based video codec. Experiments were conducted on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2023 Task 2 dataset, covering four machine types (fan, valve, toycar, slider), with anomaly detection performed using the official Autoencoder baseline model released in DCASE 2024. Detection scores were computed from reconstruction error and Mahalanobis distance. The results show that the proposed FCM-based ACoM (Audio Coding for Machines) achieves comparable or superior performance to AAC at less than half the bitrate, reliably preserving critical features even under ultra-low bitrate conditions (1.3–6.3 kbps). While VVC retains competitive performance only at high bitrates, it degrades sharply at low bitrates. These findings demonstrate that feature-based compression offers a promising direction for next-generation ACoM standardization, enabling efficient and robust ASD in bandwidth-constrained industrial environments. Full article

The introduction of Maritime Autonomous Surface Ships (MASS) and the accelerating digitalization of ports require precise and dynamic analysis of traffic conditions. However, conventional marine traffic analyses have been limited to low-resolution grids and static density visualizations without fully integrating vessel direction and speed. To address this limitation, this study proposes a traffic flow visualization model that incorporates dynamic maritime traffic structure. The model integrates density, dominant direction, and average speed into a single symbol, thereby complementing the limitations of static analyses. In addition, high-resolution grids of approximately 90 m were applied to enable detailed analysis. AIS data collected between 2022–2023 from the coastal waters of Mokpo, South Korea, were preprocessed, aggregated into grid cells, and analyzed to estimate representative directions (at 10° intervals) as well as average speeds. These results were visualized through color, thickness, length, and direction of arrows. The analysis showed high-density, low-speed traffic patterns and starboard-passage behavior in port approaches and narrow channels, while irregular directions with low density were observed in non-standard routes. The proposed model provides a visual representation of dynamic traffic structures that cannot be revealed by density maps alone, thus offering practical applicability for MASS route planning, VTS operation support, and risk assessment. Full article

In this paper, an underwater salvage robot is developed to retrieve foreign objects scattered in nuclear reactor pools. The robot mainly consists of an ROV platform and a 3-DOF Delta robotic arm. Utilizing fused IMU and LED beacon visual data for localization, it achieves pool traversal via six dynamically controlled thrusters. An improved YOLOv8s algorithm is employed to identify foreign objects in underwater environments. During traversal, the robot identifies and retrieves foreign objects along the way. The prototype of the robot was subjected to a series of experiments in an indoor pool. Results show that the improved YOLOv8 algorithm achieves 92.2% mAP, surpassing the original YOLOv8s and Faster-RCNN by 3.7 and 3.3 percentage points, respectively. The robot achieved a foreign-object identification rate of 95.42% and a retrieval success rate of 90.64% under dynamic traversal conditions, indicating that it meets the operational requirements and has significant engineering application value. Full article

This paper presents the Women’s Wise Walkshops (WWW) project, a participatory feminist methodology for urban co-design implemented in Ferrara, Italy. The research explores how women’s situated knowledge and lived experiences can inform inclusive urban planning through collaborative urban traversals and participatory design processes. Drawing on feminist epistemologies and combining elements of flâneuserie and Situationist dérive, the WWW methodology employs a seven-phase approach including semi-structured interviews, focus groups, urban walkshops, and collective mapping exercises. The study involved approximately 110 women across two distinct neighborhoods—Arianuova-Giardino and Krasnodar—representing diverse socio-demographic backgrounds. Through a thematic analysis of interviews, visual documentation, and post-walkshop discussions, six key thematic clusters emerged: safety, public space, mobility systems, community spaces and associations, public services for citizens, and participatory processes. The findings reveal that women’s perspectives from marginalized positions provide critical insights into urban inequalities and offer transformative visions for more inclusive cities. The research shows that structured participatory processes not only generate valuable urban policy recommendations but also foster community cohesion, democratic engagement, and spatial justice. The WWW methodology represents a significant contribution to feminist urban studies and participatory planning, offering a replicable framework for integrating women’s voices into urban governance and design processes. Full article