Unmanned aerial vehicle (UAV) vision systems have been widely deployed for aerial monitoring applications, yet small-target detection in UAV imagery remains a significant challenge due to minimal pixel representation, substantial scale variations, complex background interference, and varying illumination conditions. Existing object detection algorithms struggle to maintain high accuracy when processing small targets with fewer than 32 × 32 pixels in UAV-captured scenes, particularly in complex environments where target-background confusion is prevalent. To address these limitations, this study proposes RMH-YOLO, a refined multi-scale architecture. The model incorporates four key innovations: a Refined Feature Module (RFM) that fuses channel and spatial attention mechanisms to enhance weak feature representation of small targets while maintaining contextual integrity; a Multi-scale Focus-and-Diffuse (MFFD) network that employs a focus-diffuse transmission pathway to preserve fine-grained spatial details from high-resolution layers and propagate them to semantic features; an efficient CS-Head detection architecture that utilizes parameter-sharing convolution to enable efficient processing on embedded platforms; and an optimized loss function combining Normalized Wasserstein Distance (NWD) with InnerCIoU to improve localization accuracy for small targets. Experimental validation on the VisDrone2019 dataset demonstrates that RMH-YOLO achieves a precision and recall of 53.0% and 40.4%, representing improvements of 8.8% and 7.4% over the YOLOv8n baseline. The proposed method attains mAP50 and mAP50:95 of 42.4% and 25.7%, corresponding to enhancements of 9.2% and 6.4%, respectively, while maintaining computational efficiency with only 1.3 M parameters and 16.7 G FLOPs. Experimental results confirm that RMH-YOLO effectively improves small-target detection accuracy while maintaining computational efficiency, demonstrating its broad application potential in diverse UAV aerial monitoring scenarios.

Unmanned Aerial Vehicle (UAV) remote sensing has become essential for real-time earth observation applications, including precision agriculture, traffic monitoring, and disaster response. However, small-target detection in UAV aerial imagery still faces critical challenges: extreme scale variation due to variable flight altitudes, background interference from complex terrain, and insufficient pixel information for tiny objects. To address these issues, this work proposes FKIFM-DETR, a real-time transformer-based detection framework leveraging multi-domain information fusion. First, a Spatial-Frequency Fusion Module (SFM) is designed to integrate spatial and frequency-domain features for capturing fine-grained target details while suppressing background noise; second, a High–Low Frequency Block (HL-Block) is introduced to separately process high-frequency local details and low-frequency global context, balancing detail retention and semantic awareness; finally, a Channel Feature Recalibration-Enhanced Feature Pyramid Network (SPCR-FPN) is employed to strengthen the interaction between shallow spatial features and deep semantic features. On the VisDrone2019 dataset, FKIFM-DETR achieves 6.3% and 5.3% improvements in mAP@0.5 and mAP@0.5:0.95 over the RT-DETR baseline, respectively; evaluations on TinyPerson and HIT-UAV datasets further demonstrate its cross-scenario applicability. These results demonstrate the potential of FKIFM-DETR for practical UAV remote sensing applications such as crowd surveillance, vehicle tracking, and emergency rescue.

Bridges are prone to damage from various factors, impacting the overall safety of transportation networks. Accurate damage identification is crucial for maintaining bridge integrity. This study proposes a novel method using encoded images and a convolutional neural network (CNN) for bridge damage identification. By converting raw acceleration data into encoded images, the data can be represented from multiple perspectives, enhancing the extraction of essential features related to bridge damage states. The method was validated using data simulated from a continuous rigid-frame bridge model. The results demonstrate that using encoded images as inputs yields a higher recall rate, precision, and F1-score compared to using acceleration responses as inputs, achieving a comprehensive accuracy of 92%. This study concludes that the combination of encoded images and CNN provides a robust approach for accurate and efficient bridge damage identification.

Simulating the stochastic traffic load has been an important issue for bridge condition evaluation. This paper proposes an improved cellular automaton for stochastic traffic load simulation with consideration of axle load and microscopic interaction. The cells, neighbors, transition rules and boundary conditions in the improved model are all redefined. The cell length can be manually defined with any value. The vehicle load can be precisely simulated using axle load if the cell length is set small or using concentrated load when a large cell length is defined. Acceleration based transition rules are proposed, and different acceleration for each vehicle can be computed to more reasonably simulate the microscopic behavior. The feasibility of the proposed model is validated through comparisons on traffic flow simulation and load effect quantification with in-field weigh in motion data and other models. The results indicate that the proposed model improves the existing models both in vehicle load simulation and traffic flow simulation and can be used for stochastic traffic load simulation for bridges with any length.

Traffic load is one of the important external loads of bridges and simulating the site-specific traffic load is important for bridge condition assessment. This study proposes a new microscopic model to simulate the stochastic traffic load of any time period at bridge site based on weigh in motion (WIM) data. A model for simulating the microscopic interaction between vehicles was first proposed, by which free driving, car following and sudden braking can be reasonably simulated. Axle-weight information from the WIM data was embedded into the model to precisely simulate the vehicle load. A new vehicle sequence generation method based on WIM data was then proposed to generate the site-specific vehicle sequence of any time period with consideration of non-stationarity. Finally, a framework for simulating the site-specific stochastic traffic load is established. The proposed framework is validated through application based on realistic WIM data in traffic-flow simulation and load effect quantification. Comparisons of simulation results with the existing methods were also conducted to show the reasonability and novelty. The results indicate that the proposed framework can produce reasonable site-specific stochastic traffic load over any time period based on WIM data.