Search Results (59)

To address the reliance on manual calibration and the performance degradation caused by loosely coupled modules in lane-wise vehicle counting under complex traffic scenarios, this paper presents a lane-wise vehicle counting system based on adaptive lane partitioning and multi-module integration. The system first applies YOLOPv2 for lane-line detection, providing the basis for lane-region partitioning. Subsequently, Hue-Saturation-Value (HSV) color segmentation, morphological processing, and contour filtering are employed to enhance the robustness of lane feature extraction. Leveraging perspective geometry, lane regions are constructed to achieve adaptive lane partitioning. For vehicle analysis, YOLOv11 is utilized for vehicle detection, and ByteTrack is adopted for multi-object tracking. These modules are combined with lane assignment to form an integrated pipeline that preserves trajectory continuity and mitigates identity loss under occlusion and motion blur. Furthermore, a PyQt5-based interactive visualization interface is developed to support video processing, real-time display, lane-region visualization, and statistical analysis of per-lane traffic flow and lane-change behaviors. Experimental results demonstrate the effectiveness and practicality of the proposed system in complex multi-lane traffic scenarios. Full article

UAV-based road traffic state monitoring and analysis have become a hotspot in current research, where road region detection serves as the prerequisite for the aforementioned applications. This paper proposes a UAV-driven edge-based lane-detection system, and a lane line semantic segmentation, modeling and road-region-detection method. Firstly, a lightweight lane line semantic segmentation model LSLNet is presented, where the strip- aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) are used in conjunction to improve feature representation ability under low computational overheads. Furthermore, the segmented lane line mask is quantified into a parametric form and the lane-level road regions are constructed by lane line spatial geometric distribution. Finally, to evaluate the performance of the proposed method, an experiment is conducted using the self-constructed UAV-Laneline3K and UAV-Roadregion200 datasets. The experimental results show that LSLNet achieves 82.73% F1-score and 72.06% mIoU on the lane line semantic segmentation task, which runs at 82 FPS with merely 0.09M parameters and 13.0 GFLOPs. For road region detection, the mIoU and F1-score reach 97.62% and 98.86%, respectively. The results demonstrate that the proposed method enables accurate and robust road region detection in complex road environments with low computational costs. Full article

Accurate semantic segmentation of roads and overlaid markings is essential for multi-camera multi-robot visual localization systems, yet lane markings occupy a tiny fraction of the image area, making them difficult to segment reliably. This paper presents a U-Net design study for semantic segmentation of imbalanced segmentation of a dominant class and two similar, minority classes, that occur on top of the dominant class. We analyze the problem of designing a multi-head U-Net for segmenting semi-synthetic Duckietown model road map images into roads, stop-line markings, and lane-line markings. The multi-head design decomposes the task into a binary road segmentation head and a ternary marking segmentation head, connected through a road-aware loss that restricts marking supervision to predicted road regions. Our work assesses the nine loss functions to approach the class imbalance problem in the marking head—including cross-entropy, focal loss, Tversky loss, Lovász-softmax, and a subset of combinations thereof. These configurations are systematically evaluated on a dataset of semi-synthetic map images generated using an evolutionary algorithm described in a previous work of the authors, where road marking classes are a minority. The Tversky–Lovász combination achieves the highest per-class IoU across all segmentation targets, being statistically significantly better than other configurations. The results demonstrate that the Tversky loss combined with a direct IoU surrogate, Lovász-softmax, is particularly effective for small-object segmentation under severe class imbalance. 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

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

Transportation panoptic perception (TPP) is a fundamental capability for both on-board and roadside monitoring systems. In this paper, we propose an end-to-end lightweight multitask model, MT-TPPNet, which jointly performs three tasks: object detection, drivable area segmentation, and lane line segmentation. To accommodate task differences while sharing a common backbone, we introduce the Asymmetric Projection with Expanded-value (APEX) mechanism, which integrates attention mechanisms with different biases to enhance performance across various tasks. We further propose the Selective Channel–Spatial Coupling (${\mathrm{SC}}^2$) mechanism, which injects complementary frequency-band information into the channel-spatial coupled features. Additionally, by using a unified loss function to simultaneously handle detection and segmentation tasks, we eliminate the need for task-specific customizations, improving both training stability and deployment flexibility. Extensive experiments on self-collected field data and public benchmarks from roadway and railway scenarios demonstrate that MT-TPPNet consistently outperforms strong baselines in terms of mAP, mIoU, and FPS. In particular, MT-TPPNet achieves a mAP50 of 83.2% for traffic object detection, a mIoU of 91.6% for drivable-area segmentation, and an IoU of 28.9% for lane-line segmentation, demonstrating the effectiveness of the proposed approach. Full article

The traditional intermittent bus lane control struggles to achieve an effective balance between bus priority and lane utilization efficiency. To address this limitation, this study proposes a dynamic control strategy that enables the borrowing of intermittent bus lanes in mixed traffic flow environments and constructs a connected vehicle control model encompassing both the target intersection and its upstream segment. First, a dynamic clearance framework is established on the dedicated lane based on the real-time speed of buses. Concurrently, the target connected and automated vehicle (CAV) predicts the traffic signal status upon its arrival at the stop line to determine its traversable zone at the bus lanes. Subsequently, a coordinated control strategy is designed for the dynamic clearance framework and the traversable zone, leading to the development of lane-changing decision models under four distinct scenarios. This approach allows CAVs to dynamically utilize residual lane resources without compromising bus operations. Finally, using average vehicle delay as the evaluation metric, a comparative simulation analysis is conducted against the traditional bus lane utilization method across four dimensions: connected vehicle penetration rate, traffic flow saturation, right-turn proportion, and bus departure frequency. The experimental results demonstrate that the proposed strategy significantly improves both bus priority and overall traffic efficiency. Full article

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance. Full article

This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R² = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article

Sustainable highway infrastructure maintenance critically depends on precise lane line detection, yet conventional inspection approaches remain resource-depleting, carbon-intensive, and hazardous to personnel. To mitigate these constraints and address the low accuracy and high parameterization of existing models, this study utilizes unmanned aerial vehicle (UAV) imagery and proposes a UAV-based highway lane line segmentation method using an improved DeepLabV3+ model that resolves multi-scale lane line segmentation challenges in UAV imagery. MobileNetV2 is used as the backbone network to significantly reduce the number of model parameters. The Squeeze-and-Excitation (SE) attention mechanism is integrated to enhance feature extraction capabilities, particularly at lane line edges. A Feature Pyramid Network (FPN) is incorporated to improve multi-scale lane line feature extraction. We introduce a novel Waterfall Atrous Spatial Pyramid Pooling (WASPP) module, utilizing cascaded atrous convolutions with strategic dilation rate adjustments to progressively expand the receptive field and aggregate contextual information across scales. The improved model outperforms the original DeepLabV3+ by 5.04% mIoU (85.30% vs. 80.26%) and 3.35% F1-Score (91.74% vs. 88.39%) while cutting parameters by 85% (8.03 M vs. 54.8 M) and reducing training time by 2 h 50 min, thereby enhancing the model’s accuracy in lane line segmentation, reducing the number of parameters, and lowering the carbon footprint. Full article

A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of Arctic sea ice has accelerated the opening of the Arctic shipping route. This paper addresses the issue of how to reasonably segment and adopt rhumb line routes to approximate the GER in the special navigational environment of the Arctic. Using historical routes, recommended routes, and geospatial data that have passed through the Arctic shipping lane as constraints, this paper proposes a waypoint optimization model based on an adaptive hybrid particle swarm optimization-genetic algorithm (AHPSOGA). Additionally, by integrating Arctic remote sensing ice condition data and the Polar Operational Limit Assessment Risk Indexing System (POLARIS), a safety assessment model tailored for this route has been developed, enabling the quantification of sea ice risks and dynamic evaluation of segment safety. Experimental results indicate that the proposed waypoint optimization model reduces the number of waypoints and voyage distance compared to recommended routes and conventional shipping industry methods. Furthermore, the AHPSOGA algorithm achieves a 16.41% and 19.19% improvement in convergence speed compared to traditional GA and PSO algorithms, respectively. In terms of computational efficiency, the average runtime is improved by approximately 12.00% and 14.53%, respectively. The risk levels of each segment of the optimized route are comparable to those of the recommended Northeast Passage route. This study provides an effective theoretical foundation and technical support for intelligent planning and decision-making for Arctic shipping routes. Full article

Dynamic wireless charging systems have emerged as a promising solution to extend the driving range of electric vehicles by enabling energy transfer while the vehicle is in motion. However, the segment-based charging lane structure introduces challenges such as pulsation of the output power and the need for precise switching control of the transmitting segments. This paper proposes a position-sensorless control method for managing transmitting lines in a dynamic wireless charging system. The proposed approach uses a segmented charging lane structure combined with two receiving coils and LCC compensation circuits on both the transmitting and receiving sides. Based on theoretical analysis, the study determines the optimal switching positions and signals to reduce the current fluctuation. To validate the proposed method, a dynamic wireless charging system prototype with a power rating of $3kW$ was designed, constructed, and tested in a laboratory environment. The results demonstrate that the proposed position-sensorless control method effectively mitigates power fluctuations and enhances the stability and efficiency of the wireless charging process. Full article

Agricultural factories utilize advanced facilities and technologies to cultivate crops in a controlled environment, enhancing operational yields and reducing reliance on natural resources. This is crucial for ensuring a stable supply of agricultural products year-round and plays a significant role in the transformation of agricultural modernization. Automated Guided Vehicles (AGVs) are commonly employed in agricultural factories due to their low ownership costs and high efficiency. However, small embedded devices on AGVs face significant challenges in managing multiple tasks while maintaining the required timeliness. Multi-task learning (MTL) is increasingly employed to enhance the efficiency and performance of detection models in joint detection tasks, such as lane-line detection, pedestrian detection, and obstacle detection. The YOLOP (You Only Look for Panoptic Driving Perception) model demonstrates strong performance in simultaneously addressing these tasks; detecting lane lines in changeable agricultural factory scenarios is yet a challenging task, limiting the subsequent accurate planning and control of AGVs. This paper proposes a feedback-based network for joint detection tasks (MTNet) that simultaneously detects pedestrians, automated guided vehicles (AGVs), and QR codes, while also performing lane-line segmentation. This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. Homemade datasets were used for training and testing. Comparative experiments of our model with different models in the target-detection and lane-line detection tasks, respectively, show the progressiveness of our model. Surprisingly, we also obtained a significant improvement in the model’s processing speed. Furthermore, we conducted ablation experiments to assess the effectiveness of our improvements in lane-line detection, all of which outperformed the original detection model. Full article

Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulation-based data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications. Full article

In the field of autonomous driving, the accurate identification of drivable areas on roads is the key to ensuring the safe driving of vehicles. However, unstructured roads lack clear lane lines and regular road structures, and they have fuzzy edges and rutting marks, which greatly increase the difficulty of identifying drivable areas. To address the above challenges, this paper proposes a drivable area detection method for unstructured roads based on the MRNet model. To address the problem that unstructured roads lack clear lane lines and regular structures, the model dynamically captures local and global context information based on the self-attention mechanism of a Transformer, and it combines the input of image and LiDAR data to enhance the overall understanding of complex road scenes; to address the problem that detailed features such as fuzzy edges and rutting are difficult to identify, a multi-scale dilated convolution module (MSDM) is proposed to capture detailed information at different scales through multi-scale feature extraction; to address the gradient vanishing problem in feature fusion, a residual upsampling module (ResUp Block) is designed to optimize the spatial resolution recovery process of the feature map, correct errors, and further improve the robustness of the model. Experiments on the ORFD dataset containing unstructured road data show that MRNet outperforms other common methods in the drivable area detection task and achieves good performance in segmentation accuracy and model robustness. In summary, MRNet provides an effective solution for drivable area detection in unstructured road environments, supporting the environmental perception module of autonomous driving systems. Full article