Search Results (536)

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Pedestrian detection, as a core function of an intelligent vision system, plays a key role in obstacle avoidance during driverless navigation, intelligent traffic monitoring, and other fields. In this paper, we optimize the YOLO11 detection algorithm to solve the problem of insufficient accuracy of pedestrian detection in complex scenes. The C3K2-lighter module is constructed by replacing the Bottleneck in the C3K2 module with the FasterNet Block, which significantly enhances feature extraction for long-distance pedestrians in dense scenes. In addition, it incorporates the Triplet Attention Module to establish correlations between local features and the global context, thereby effectively mitigating omission problems caused by occlusion. The Variable Focus Loss Function (VFL) is additionally introduced to optimize target classification by quantifying the variance in features between the predicted frame and the ground-truth frame. The improved model, YOLO11-Improved, achieves a synergistic optimization of detection accuracy and computational efficiency, increasing the AP value by 3.7% and the precision by 2.8% and reducing the parameter volume by 0.5 M while maintaining real-time performance. Full article

To address the limitation of receptive fields caused by the use of local convolutions in current point cloud object detection methods, this paper proposes a LiDAR point cloud object detection algorithm that integrates global features. The proposed method employs a Voxel Mapping Block (VMB) and a Global Feature Extraction Block (GFEB) to convert the point cloud data into a one-dimensional long sequence. It then utilizes non-local convolutions to model the entire voxelized point cloud and incorporate global contextual information, thereby enhancing the network’s receptive field and its capability to extract and learn global features. Furthermore, a Voxel Channel Feature Extraction (VCFE) module is designed to capture local spatial information by associating features across different channels, effectively mitigating the spatial information loss introduced during the one-dimensional transformation. The experimental results demonstrate that, compared with state-of-the-art methods, the proposed approach improves the average precision of vehicle, pedestrian, and cyclist targets on the Waymo subset by 0.64%, 0.71%, and 0.66%, respectively. On the nuScenes dataset, the detection accuracy for var targets increased by 0.7%, with NDS and mAP improving by 0.3% and 0.5%, respectively. In particular, the method exhibits outstanding performance in small object detection, significantly enhancing the overall accuracy of point cloud object detection. Full article

Pedestrian localization is a fundamental sensing task for intelligent outdoor systems. To overcome the limitations of accuracy and efficiency in conventional binocular approaches, this study introduces a trinocular stereo vision framework that integrates template matching with geometric constraint optimization. The system employs a trinocular camera configuration arranged in an equilateral triangle, which enables complementary perspectives beyond a standard horizontal baseline. Based on this setup, an initial depth estimate is obtained through multi-scale template matching on the primary binocular pair. The additional vertical viewpoint is then incorporated by enforcing three-view geometric consistency, yielding refined and more reliable depth estimates. We evaluate the method on a custom outdoor trinocular dataset. Experimental results demonstrate that the proposed approach achieves a mean absolute error of 0.435 m with an average processing time of 3.13 ms per target. This performance surpasses both the binocular Semi-Global Block Matching (0.536 m) and RAFT-Stereo (0.623 m for the standard model and 0.621 m for the real-time model without fine-tuning). When combined with the YOLOv8-s detector, the system can localize pedestrians in 7.52 ms per frame, maintaining real-time operation (> 30 Hz) for up to nine individuals, with a total end-to-end latency of approximately 32.56 ms. Full article

Kazakhstan holds the global leadership position in natural uranium mining. Nonetheless, the extraction and processing of radioactive ores has the potential to induce instances of radiological contamination. This study aimed to evaluate the radiological soil contamination at a former monazite, tin, and radioactive ore processing facility located in Ust-Kamenogorsk city. Pedestrian gamma–ray measurements revealed dose rates up to 1.00 µSv/h, significantly exceeding the natural background (0.16–0.18 µSv/h). The analysis of the 28 soil profiles demonstrated that deeper soil layers (below 60 cm) were significantly contaminated with radionuclides constituting production waste. Furthermore, the total activity in the superficial soil layer is in the range of 583–5275 Bq/kg (alpha emitters) and 641–1749 Bq/kg (beta radionuclides). The maximum of total radioactivity in the samples collected at the 80–100 cm layer was at the level of 22,482 Bq/kg (α-emitters) and 6845 Bq/kg for gross beta radiation. In consideration of the site’s proximity to public buildings, the calculated radiological hazard indices were calculated, revealing the potential danger for human health. The elevated excess lifetime cancer risk and annual gonadal dose equivalent obtained for the topsoil layer indicate a high level of radiological risk to the local population. The obtained results emphasise the necessity of developing rehabilitation strategies and long-term monitoring of the contaminated site, which is consistent with the global objectives of sustainable development in the field of environmental protection and public health. Full article

The traditional Arab-Islamic urban fabric of Riyadh, with its emphasis on privacy, social cohesion, and environmental adaptation, was radically disrupted when the 1970s Doxiadis master plan was implemented, transforming the city into a car-dependent gridiron design. The shift led to ever-spreading sprawl, reduced pedestrian-friendliness, and eroded local identity. Using Hegelian dialectics methodology, this paper proposes integration of fused grid theory and urban Islamic fractals geometry in the urban fabric of the city. Specifically for Riyadh, the proposed changes encourage urban quadrant nesting, change of block scale and layout, fractal landscape integration, and multi-modal permeability. These adaptations are intended to increase connectivity, reduce crash rates, minimize impacts on the environment, enhance walkability, and elevate overall quality of life. Full article

With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in complex urban environments, this paper proposes an improved YOLOv8-based detection algorithm, termed TFP-YOLO, designed to recognize traffic signs such as traffic lights and crosswalks, as well as small obstacle objects including pedestrians and bicycles, thereby improving the target detection performance of machine guide dogs in complex road scenarios. The proposed algorithm incorporates a Triplet Attention mechanism into the backbone network to strengthen the perception of key regions, and integrates a Triple Feature Encoding (TFE) module to achieve collaborative extraction of both local and global features. Additionally, a P2 detection head is introduced to improve the accuracy of small object detection, particularly for traffic lights. Furthermore, the WIoU loss function is adopted to enhance training stability and the model’s generalization capability. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.9% and a precision of 90.2%, while reducing the number of parameters by 17.2%. These improvements significantly enhance the perception performance of machine guide dogs in identifying traffic information and obstacles, providing strong technical support for subsequent path planning and embedded deployment, and demonstrating considerable practical application value. Full article

Communications in intelligent transportation systems (ITS) face explosive data growth from applications such as autonomous driving, remote diagnostics, and real-time monitoring, imposing severe challenges on limited spectrum, bandwidth, and latency. Reliable semantic image reconstruction under noisy channel conditions is critical for ITS perception tasks, since noise directly impacts the recognition of both static infrastructure and dynamic obstacles. Unlike traditional approaches that aim to transmit all image data with equal fidelity, effective ITS communication requires prioritizing task-relevant dynamic elements such as vehicles and pedestrians while filtering out largely static background features such as buildings, road signs, and vegetation. To address this, we propose an Offline Knowledge Base and Attention-Driven Semantic Communication (OKBASC) framework for image-based applications in ITS scenarios. The proposed framework performs offline semantic segmentation to build a compact knowledge base of semantic masks, focusing on dynamic task-relevant regions such as vehicles, pedestrians, and traffic signals. At runtime, precomputed masks are adaptively fused with input images via sparse attention to generate semantic-aware representations that selectively preserve essential information while suppressing redundant background. Moreover, we introduce a further Bi-Level Routing Attention (BRA) module that hierarchically refines semantic features through global channel selection and local spatial attention, resulting in improved discriminability and compression efficiency. Experiments on the VOC2012 and nuPlan datasets under varying SNR levels show that OKBASC achieves higher semantic reconstruction quality than baseline methods, both quantitatively via the Structural Similarity Index Metric (SSIM) and qualitatively via visual comparisons. These results highlight the value of OKBASC as a communication-layer enabler that provides reliable perceptual inputs for downstream ITS applications, including cooperative perception, real-time traffic safety, and incident detection. Full article

Background/Objectives: Due to its potent local anesthetic and vasoconstrictive properties, cocaine is sometimes used in otolaryngologic surgical interventions. However, cocaine topical administration is not always adequately documented by practitioners, which can lead to serious legal consequences, particularly in the context of drug-impaired driving (DUID) investigations. This study retrospectively analyzes five road accident cases where cocaine was detected in biological samples after medical interventions. Case descriptions: Following pedestrian–car, or car–car accidents, five distinct patients aged between 30 and 84 years underwent maxillofacial surgery due to significant injuries. Given the severity of the accident and the circumstances, the police requested blood toxicological analysis to determine whether the patients were under the influence of psychoactive substances at the moment of the accidents. Results: The five cases described in this manuscript had blood cocaine concentrations exceeding the Swiss legal limit for drivers (15 µg/L). Since no information was initially provided about the medical use of cocaine after the crash, recreational use of cocaine was suspected. However, subsequent investigations confirmed that the cases involved medical administration. Conclusions: After sinonasal procedures involving the topical application of cocaine, patients may yield positive results on urine and blood drug tests, potentially resulting in serious legal repercussions, including the withdrawal of their driving license. Therefore, practitioners should thoroughly document the medical use of topical cocaine, particularly in DUID cases. These results also raise questions about the benefit–risk ratio of such use, considering that alternatives exist. Full article

Gulf cities have embarked on ambitious public transport infrastructure initiatives in recent decades to foster more livable and sustainable cities. This investigation explores the interpretations and implementation of Transit-Oriented Development (TOD) principles in two prototypical urban districts: Doha’s West Bay, Qatar, and Riyadh’s King Abdullah Financial District (KAFD), Saudi Arabia. By following a comparative case study approach, the study explores how retrofitted (West Bay) and purpose-built (KAFD) TOD configurations fare regarding land use mix, density, connectivity, transit access, and environmental responsiveness. The comparative methodology was selected to specifically capture the spatial, climatic, and socio-economic complexities of TOD implementation in hyper-arid urban environments. Based on qualitative evidence from stakeholder interviews, spatial assessments, and geospatial indicators—such as metro access buffers, building shape compactness, and TOD proximity classification—the investigation reflects both common challenges and localized adaptations in hot-desert Urbanism. It emerges that, while benefiting from integrated planning and multimodal connectivity, KAFD’s pedestrian realm is delimited by climatic constraints and inactive active transport networks. West Bay, on the other hand, features fragmented public spaces and low TOD cohesion because of automotive planning heritages. However, it holds potential for retrofit through infill development and tactical Urbanism. The results provide transferable insights that can inform TOD strategies in other Gulf and international contexts facing similar sustainability and mobility challenges. By finalizing strategic recommendations for urban livability improvement through context-adaptive TOD approaches in Gulf cities, the study contributes to the wider discussion of sustainable Urbanism in rapidly changing environments and supplies a reproducible assessment frame for future TOD planning. This study contributes new knowledge by advancing a context-adaptive TOD framework tailored to the unique conditions of hyper-arid Gulf cities. Full article

In the field of modern computer vision, pedestrian detection technology holds significant importance in applications such as intelligent surveillance, autonomous driving, and robot navigation. However, single-modal images struggle to achieve high-precision detection in complex environments. To address this, this study proposes a GLNet-YOLO framework based on cross-modal deep feature fusion, aiming to improve pedestrian detection performance in complex environments by fusing feature information from visible light and infrared images. By extending the YOLOv11 architecture, the framework adopts a dual-branch network structure to process visible light and infrared modal inputs, respectively, and introduces the FM module to realize global feature fusion and enhancement, as well as the DMR module to accomplish local feature separation and interaction. Experimental results show that on the LLVIP dataset, compared to the single-modal YOLOv11 baseline, our fused model improves the mAP@50 by 9.2% over the visible-light-only model and 0.7% over the infrared-only model. This significantly improves the detection accuracy under low-light and complex background conditions and enhances the robustness of the algorithm, and its effectiveness is further verified on the KAIST dataset. Full article

In the process of revitalizing historic districts, creating a healthy living environment requires a focus on the microclimate comfort of historic districts. Microclimate comfort refers to the comprehensive physiological perception and psychological satisfaction of climate elements such as heat, wind, and humidity under specific local environmental conditions, typically within a spatial range of horizontal scale < 100 m and vertical scale < 10 m. Among these, wind environment quality, as a key factor influencing pedestrian health experiences and cultural tourism appeal, holds particular research value. This study takes the Dabao Island Courtyard District in Qingdao as its subject, employing computational fluid dynamics (CFD) simulation methods from the artificial intelligence (AI) technology framework for modeling. CFD is a numerical method based on computer simulation, which solves fluid control equations (such as the Navier–Stokes equations) through iterative optimization to achieve high-fidelity simulation of physical environments such as airflow, turbulence, and heat transfer. A three-dimensional geometric model of the Dabao Island courtyard district was established, and boundary conditions were set based on local meteorological data. Numerical simulations were conducted to analyze the wind environment before and after the renovation of different layouts, functional spaces, and spatial scales (individual courtyards, clustered courtyards, and surrounding neighborhoods) of the courtyard district. The results indicate that factors such as building layout, street orientation, and renovation strategies significantly influence the wind environment of the Dabao Island neighborhood courtyards, thereby affecting residents’ perceptions of wind comfort. For example, unreasonable building layouts can lead to excessive local wind speeds or vortex phenomena, reducing wind comfort, whereas reasonable renovation and update strategies can facilitate the introduction of wind corridors into the historical courtyard buildings, improving wind environment quality. This study contributes to better protection and utilization of traditional neighborhoods during urban renewal processes, creating a more comfortable wind environment for residents, providing scientific decision-making support for the renovation of historical neighborhoods under the Healthy China strategy, and offering methodological references for wind environment research in other similar traditional neighborhoods. Full article

This article focuses on how to further explore the impact of building layout and form on the local wind environment in micro scale ventilation corridors connected to the urban scale. Taking Shijiazhuang as the research area, three typical blocks of complex building forms, including old and new ones, were selected near the built ventilation corridors. CFD numerical simulation and on-site observation experiments were conducted to analyze the impact of different building heights and layouts on the wind environment in each typical block qualitatively and quantitatively. The above can provide a reference and guidance for the construction of secondary and tertiary ventilation corridors and the spatial form design of functional buildings during urban renewal in the stock era. The results show the following: (1) average wind speed, Mean Wind Velocity ratio, and the proportion of the outdoor pedestrian comfort zone are negatively correlated with the building height, but there is a threshold for them to decrease with the increase in the building height. Observation experiments also indicate that in the background of the south wind, the internal and leeward wind environment of new high-rise residential areas is better than that of old low residential areas. (2) Regression analysis was conducted between the simulated average wind speed and the building height, indicating that regulating the average building height to be below 45 m can improve the wind environment as the building height decreases. (3) The enclosed building complex has the smallest impact distance on downstream wind speed compared to point, row, and staggered layouts, but its internal ventilation environment is relatively poor. To ensure the ventilation performance, the upper limit of the building height should be stricter, and it should be controlled within at least 40 m, especially below 30 m. (4) In the process of urban renewal in the future, it is recommended to conduct an overall ventilation efficiency evaluation for different blocks. Compared to others, increasing the height of buildings and leaving more space to increase the inter site ratio/building spacing is more beneficial for the overall ventilation environment. Full article

Shared cargo bike services hold significant potential for promoting sustainable urban mobility, yet their adoption remains limited—especially for private, everyday use. This study investigates how such systems can be more effectively integrated into urban transport by identifying key enablers and operationalizing them through a GIS-based multi-criteria analysis (MCA). Using a mixed-methods approach, expert interviews were conducted to explore success factors and barriers. Results highlight the dual function of shared cargo bikes: enabling occasional use while increasing long-term uptake by fostering trial and visibility. The study identifies both spatial and non-spatial enablers. Key spatial factors include high visibility, pedestrian flows, access to public transport and cycling networks, and placement in mixed-use areas. Non-spatial enablers include technical reliability, ease of use, strong visual identity, subsidies, and trial opportunities. The spatial enablers were operationalized into seven criteria in the MCA. Based on qualitative expert interviews and thematic analysis, the highest weights were assigned to visibility and pedestrian flows, followed by proximity to public transport and local centers, while lower weights were given to proximity to residences, population density, and access to cycle paths. The results offer guidance for station placement and demonstrate the role of shared cargo bikes in sustainable urban transport. Full article

Remote Sighted Assistance (RSA) systems provide visually impaired people (VIPs) with real-time guidance by connecting them with remote sighted agents to facilitate daily travel. However, unfamiliar environments often complicate decision-making for agents and can induce anxiety in VIPs, thereby reducing the effectiveness of the assistance provided. To address this challenge, this paper proposes a video-based map assistance method. By pre-recording pedestrian path videos and aligning them with geographic locations, the system enables route preview and enhances navigation guidance. This study introduces a factor graph optimization (FGO) algorithm that integrates Global Navigation Satellite System (GNSS) and pedestrian dead reckoning (PDR) data for pedestrian positioning. It incorporates road-anchor constraints, a turning-point-based anchor-matching method, and a coarse-to-fine optimization strategy to improve the positioning accuracy. GNSS provides global reference positions, PDR offers precise relative motion constraints through accurate heading estimation, and anchor factors further enhance localization accuracy by leveraging known geometric features. We collected data using a smartphone equipped with a four-camera module and conducted tests in representative urban environments. Experimental results demonstrate that the proposed anchor-aided FGO-GNSS/PDR algorithm achieves robust and accurate positioning, effectively supporting video-based map construction in complex urban settings. With anchor constraints, the mean horizontal positioning error was reduced by 42% to 65% and the maximum error by 38% to 76% across all datasets. In this study, the mean horizontal positioning error was 1.36 m. Full article