Search Results (8)

Object detection from unmanned aerial vehicle (UAV) imagery is essential for applications such as traffic monitoring, disaster response, and urban surveillance, yet most existing methods are developed and evaluated under clear-sky conditions. In real-world UAV operations, adverse weather including fog, rain, and snow introduces severe image degradation that simultaneously disrupts both the geometric and photometric properties of targets. This paper identifies two fundamental bottlenecks underlying this performance collapse: the lack of geometric invariance in standard convolutional operators and the inability of fixed receptive fields to reconstruct features corrupted by atmospheric interference. To address these bottlenecks, we propose SELPNet (Scene Exploration and Large Format Perception Network), a unified framework that integrates geometric alignment and multi-scale contextual perception into the YOLOv13 head. SELPNet consists of two key modules: (1) The Scene Exploration Convolution (SEC) leverages affine Lie group theory to construct a discrete manifold of rotation and scale transformations, actively probing multiple geometric views and selecting the most coherent response via a Maxout mechanism. (2) The Large Format Perception Module (LPM) introduces a dynamic dilation strategy with depthwise separable convolutions, progressively enlarging the receptive field from fine-grained edge preservation to scene-level contextual perception for semantic completion of degraded regions. We further construct and release AWU-OBB, a large-scale benchmark containing over 18,000 oriented bounding box-annotated UAV images across four representative scene categories. Ablation experiments demonstrate that SEC and LPM yield complementary gains, achieving a combined improvement of +4.26% mAP50 over the YOLOv13-n baseline with only 0.11 M additional parameters and 0.2 extra GFLOPs. The source code will be publicly released upon acceptance of this paper. Full article

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance. Full article

Road agglomerate fog seriously threatens driving safety, making real-time fog state detection crucial for implementing reliable traffic control measures. With advantages in aerial perspective and a broad field of view, UAVs have emerged as a novel solution for road agglomerate fog monitoring. This paper proposes an agglomerate fog detection method based on the fusion of SURF and optical flow characteristics. To synthesize an adequate agglomerate fog sample set, a novel network named FogGAN is presented by injecting physical cues into the generator using a limited number of field-collected fog images. Taking the region of interest (ROI) for agglomerate fog detection in the UAV image as the basic unit, SURF is employed to describe static texture features, while optical flow is employed to capture frame-to-frame motion characteristics, and a multi-feature fusion approach based on Bayesian theory is subsequently introduced. Experimental results demonstrate the effectiveness of FogGAN for its capability to generate a more realistic dataset of agglomerate fog sample images. Furthermore, the proposed SURF and optical flow fusion method performs higher precision, recall, and F1-score for UAV perspective images compared with XGBoost-based and survey-informed fusion methods. Full article

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse motion patterns, varying clothing colors, occlusions, and positional differences. This study introduces an innovative approach that integrates multiple surveillance cameras at signalized intersections, regardless of their types or resolutions. Two distinct convolutional neural network (CNN)-based detection algorithms accurately track road users across multiple views. The resulting trajectories undergo analysis, smoothing, and integration, enabling detailed traffic scene reconstruction and precise identification of vehicle–pedestrian conflicts. The proposed framework achieved 97.73% detection precision and an average intersection over union (IoU) of 0.912 for pedestrians, compared to 68.36% and 0.743 with a single camera. For vehicles, it achieved 98.2% detection precision and an average IoU of 0.955, versus 58.78% and 0.516 with a single camera. These findings highlight significant improvements in detecting and analyzing traffic conflicts, enhancing the identification and mitigation of potential hazards. Full article

Unmanned Aerial Vehicles (UAVs) face collision risks due to Beyond Visual Line of Sight operations. Therefore, UAV Traffic Management (UTM) systems are used to manage and monitor UAV flight paths. However, centralized UTM systems are susceptible to various security attacks and are inefficient in managing flight data from different service providers. It further fails to provide low-latency communication required for UAV real-time operations. Thus, this paper proposes to integrate Drone-to-Drone (D2D) communication protocol into a secure public blockchain-based UTM system to enable direct communication between UAVs for efficient collision avoidance. The D2D protocol is designed using SHA256 hash function and bitwise XOR operations. A proof of concept has been built to verify that the UTM system is secure by enabling authorized service providers to view sensitive flight data only using legitimate secret keys. The security of the protocol has been analyzed and has been proven to be secure from key disclosure, adversary-in-the-middle, replay, and tracking attacks. Its performance has been evaluated and is proven to outperform existing studies by having the lowest computation cost of 0.01 ms and storage costs of 544–800 bits. Full article

This study presents a comprehensive methodology for urban traffic monitoring and infrastructure decision making, centered on synchronous, simultaneous aerial data collection through a distributed multi-point UAV deployment. Conducted in the GreenLine district of Astana, Kazakhstan, the research utilized a coordinated fleet of UAVs to capture real-time video footage at 30 critical observation points during peak traffic periods, enabling a network-wide view of traffic dynamics. The collected data were processed to extract key traffic parameters, such as flow rates, vehicle speeds, and delays, which informed the calibration of a detailed traffic simulation model. Based on this model, six infrastructure development scenarios were evaluated using a multi-criteria decision-making framework to identify the most effective intervention strategies. This study introduces a replicable, data-driven approach that links synchronized UAV sensing with simulation-based evaluation, offering a practical decision support tool for improving urban infrastructure performance within the context of smart and rapidly evolving cities. Full article

mmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are carried out in a variety of practical scenarios including pedestrians, a car, and an UAV, also known as a drone. With measured data, range-angle maps are created, and morphological operators are used to estimate the AoA of the targets. We also process radar range-angle images for improved visual representation. The proposed method will be extremely beneficial for practical ground stations, traffic control and monitoring frameworks for both on-ground and airborne vehicles. Full article

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic
monitoring, video surveillance, and navigation. Full article