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Effective asset management is crucial in transportation systems, guaranteeing the optimal use, upkeep, and durability of infrastructure, which in turn boosts the safety, efficiency, and sustainability of travel networks. Traffic signs management, as a vital component of asset management, plays a key role in maintaining road safety and efficiency; however, their maintenance demands substantial time and resources investment. The advent of advanced sensing technologies in intelligent transportation systems (ITS) presents an opportunity for more effective and precise asset management. Yet, the challenge lies in the lack of localized traffic sign data in the US, which hinders the implementation of these technologies. To address this gap, our research introduces a traffic sign detection and recognition (TSDR) architecture designed to automatically collect traffic sign information and establish a US-specific data inventory. Recognizing the limitations of existing public traffic sign data sets, which are not tailored for US traffic signs, we collected an additional 5,000 traffic sign images from the Washington State area using Google Map application programming interface (API) and self-installed dash cameras. These signs were manually labeled into 43 classes for training purposes. With the training process, the proposed TSDR model can achieve impressive accuracy (98.34% in detection and 97.10% in recognition). In summary, we developed an automated pipeline, TSDR, for capturing, detecting, classifying, and storing traffic signs, culminating in the creation of a localized traffic sign data inventory for the US. [ABSTRACT FROM AUTHOR]

In response to the frequent occurrence of rock blasting during traffic tunnel excavation under high ground stress, this paper presents a detailed introduction to microseismic monitoring technology. Firstly, the principle and role of microseismic monitoring are explained, including the characteristics and main processes of microseismic monitoring technology. Secondly, the characteristics of microseismic monitoring technology are introduced, and the different characteristics encountered in the application of microseismic monitoring technology to traffic tunnels are discussed. The introduction of the microseismic monitoring process includes six parts: microseismic signal acquisition, recognition and classification, noise reduction, arrival detection, localization, and microseismic-based forecast and warning. Finally, an outlook on the development of microseismic monitoring in traffic tunnels is given. [ABSTRACT FROM AUTHOR]

Map matching is a fundamental prerequisite for traffic engineers in detecting congestion using location data represented by trajectory data. Previous studies often revolve around road matching, yet limitations arise from trajectory data quality and map-matching accuracy. This paper introduces a map-independent congestion identification method, involving urban cell network construction, congestion modeling with speed fluctuations, and the exploration of congestion evolution patterns. Finally, we validated our proposed method using Floating Taxi Data (FTD) from Xi'an, China. The result indicates that the method proposed in this study can identify urban traffic congestion and uncover its evolutionary characteristics without relying on maps. In contrast to other metrics, the customized congestion value considers the impact of speed fluctuations on congestion. The method proposed in this paper offers a benchmark solution for characterizing urban traffic congestion and formulating travel guidelines. [ABSTRACT FROM AUTHOR]

Ship orientation detection is essential for maritime navigation, traffic monitoring, and defense, yet existing methods face challenges with rotational invariance in large-angle scenarios, difficulties in multi-scale feature fusion, and the limitations of traditional IoU when detecting oriented objects and predicting objects' orientation. In this article, we propose a novel ship orientation detection (RACR-ShipDet) network based on rotation-adaptive ConvNeXt and Enhanced RepBiFPAN in remote sensing images. To equip the model with rotational invariance, ConvNeXt is first improved so that it can dynamically adjust the rotation angle and convolution kernel through adaptive rotation convolution, namely, ARRConv, forming a new architecture called RotConvNeXt. Subsequently, the RepBiFPAN, enhanced with the Weighted Feature Aggregation module, is employed to prioritize informative features by dynamically assigning adaptive weights, effectively reducing the influence of redundant or irrelevant features and improving feature representation. Moreover, a more stable version of KFIoU is proposed, named SCKFIoU, which improves the accuracy and stability of overlap calculation by introducing a small perturbation term and utilizing Cholesky decomposition for efficient matrix inversion and determinant calculation. Evaluations using the DOTA-ORShip dataset demonstrate that RACR-ShipDet outperforms current state-of-the-art models, achieving an mAP of 95.3%, representing an improvement of 5.3% over PSC (90.0%) and of 1.9% over HDDet (93.4%). Furthermore, it demonstrates a superior orientation accuracy of 96.9%, exceeding HDDet by a margin of 5.0%, establishing itself as a robust solution for ship orientation detection in complex environments. [ABSTRACT FROM AUTHOR]

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. [ABSTRACT FROM AUTHOR]

With the rapid development of infrared (IR) imaging UAV technology, infrared aerial image processing technology has been applied in different fields. But it is not very convenient to obtain real aerial images in some cases because of flight limitations, acquisition costs and other factors. So, it is necessary to simulate UAV infrared images by computer. This paper proposed an improved infrared aerial image simulation method based on open source AirSim. By improving the original AirSim infrared image simulation method, the simulation quality of the infrared image is improved via 3-dimensional segmented model processing. The infrared aerial images of the traffic scene with different viewing angles are simulated via the proposed method in this paper and we constructed infrared traffic scene simulation dataset (IR-TSS) containing seven types of objects. We propose the efficient EfficientNCSP-Net net for the IR-TSS dataset and use popular methods for comparative experiments. The experimental results show that the proposed EfficientNCSP-Net has an mAP50 greater than 96% for object detection on IR-TSS dataset, which is better than those of the existing methods. This paper not only contributes to research on infrared image simulations of traffic scenes, but also has referential significance in other aerial image simulation fields. [ABSTRACT FROM AUTHOR]

The development of China's digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. [ABSTRACT FROM AUTHOR]

Air Traffic Services play a crucial role in the safety, security, and efficiency of air transportation. The International Civil Aviation Organization (ICAO) performance-based surveillance concept requires monitoring the actual performance of the surveillance systems underpinning these services. This assessment is usually based on the analysis of data gathered during the normal operation of the surveillance systems, also known as opportunity traffic. Processing opportunity traffic requires data association to identify and assign the sensor detections to a flight. Current techniques for association require expert knowledge of the flight dynamics of the target aircraft and have issues with high-manoeuvrability targets like military aircraft and Unmanned Aircraft (UA). This paper addresses the data association problem through the use of the Multi-Sensor Intelligent Data Association (M-SIOTA) algorithm based on Deep Neural Networks (DNNs). This is an innovative perspective on the data association of multi-sensor surveillance through the lens of machine learning. This approach enables data processing without assuming any dynamics model, so it is applicable to any aircraft class or airspace structure. The proposed algorithm is trained and validated using several surveillance datasets corresponding to various phases of flight and surveillance sensor mixes. Results show improvements in association performance in the different scenarios. [ABSTRACT FROM AUTHOR]

Traffic congestion detection in surveillance video is crucial for road traffic condition monitoring and improving traffic operation efficiency. Currently, traffic congestion is often characterized through traffic density, which is obtained by detecting vehicles or using holistic mapping methods. However, these traditional methods are not effective in dealing with the vehicle scale variation in surveillance video. This prompts us to explore density-map-based traffic density detection methods. Considering the dynamic characteristics of traffic flow, relying solely on the spatial feature of traffic density is overly limiting. To address these limitations, we propose a multi-task framework that simultaneously estimates traffic density and dynamic traffic congestion. Specifically, we firstly propose a Selective Scale-Aware Network (SSANet) to generate a traffic density map. Secondly, we directly generate a static congestion level from a traffic density map through a linear layer, which can characterize the spatial occupancy of traffic congestion in each frame. In order to further describe dynamic congestion, we simultaneously consider the dynamic characteristics of traffic flow, using the overall traffic flow velocity integrated with static congestion estimation for a dynamic assessment of congestion. On the collected dataset, our method achieves state-of-the-art results on both congestion detection and density estimation task. SSANet also obtains 99.21% accuracy on the UCSD traffic flow classification dataset, which outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks. [ABSTRACT FROM AUTHOR]

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm's ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network's training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios. [ABSTRACT FROM AUTHOR]

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time. [ABSTRACT FROM AUTHOR]

This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average Accuracy p i x e l of 96.4% and Accuracy l o g s of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology's success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors. [ABSTRACT FROM AUTHOR]

Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these visualizations in real time presents serious difficulties. This paper introduces ApproxGeoMap, a novel system designed to efficiently generate approximate geo-maps from fast-arriving georeferenced data streams. ApproxGeoMap employs a stratified spatial sampling method, leveraging geohash tessellation and Earth Mover's Distance (EMD) to maintain both accuracy and processing speed. We developed a prototype system and tested it on real-world smart city datasets, demonstrating that ApproxGeoMap meets time-based and accuracy-based quality of service (QoS) constraints. Results indicate that ApproxGeoMap significantly enhances efficiency in both running time and map accuracy, offering a reliable solution for high-speed data environments where traditional methods fall short. [ABSTRACT FROM AUTHOR]

Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has demonstrated promising capabilities for routing optimization in Software-Defined Networks (SDNs). However, existing DRL-based routing algorithms are struggling to extract graph-structured information and constrained to a fixed topology, suffering from the lack of robustness. In this paper, we strengthen the advantages of Graph Neural Networks (GNNs) for DRL-based routing optimization and propose a novel algorithm named Graph Transformer Star Routing (GTSR) to enhance robustness against topology changes. GTSR utilizes the multi-agent architecture to enable each node to make routing decisions independently, and introduces a Graph Transformer to equip agents with the capabilities of handling topology changes. Furthermore, we carefully design a global message-passing mechanism with a virtual star node and a path-based readout method, enhancing the long-range perception of traffic and the detection of potential congestion for routing decision-making. Moreover, we construct a multi-agent cooperation mechanism to facilitate the learning of universal perceptual strategies and reduce the amount of computation. Extensive experiments on multiple real-world network topologies demonstrate that GTSR is capable of adapting to unseen topology changes without retraining and decreases end-to-end latency by at least 47% and packet loss rate by at least 10% compared to all baselines, highlighting strong robustness. [ABSTRACT FROM AUTHOR]

In the era of mobile Internet, the widespread use of VPNs increases the demand for data security and privacy but also poses challenges for ISPs in terms of quality of service and traffic monitoring. The research in this paper focuses on how to accurately classify encrypted traffic. Traditional methods usually require manual labeling of features, which suffers from high cost and unstable accuracy. Due to the special characteristics of encrypted traffic, traditional labeling methods cannot be well adapted, so new solutions are urgently needed. In this paper, a generative learning method based on large-scale language models is adopted, which fuses encrypted traffic features into the T5 language model. The fine-tune T5 model conducts transfer learning with a small amount of data and achieve good classification accuracy. Compared with the traditional methods, the model performs better in terms of classification effectiveness. It can effectively classify encrypted traffic even with a small number of samples, and distinguish between VPN and non-VPN traffic. Test results on the ISCX VPN-nonVPN dataset show that the new generative classifier improves the F1 score to 98.5%, which is a 5.5% improvement compared to the previous one. The experiments show that the method is effective and efficient. [ABSTRACT FROM AUTHOR]

Accurate traffic sign recognition is one of the core technologies of intelligent driving systems, which face multiple challenges such as insufficient light and shadow interference at night. In this paper, we improve the YOLOv5 model for small, fuzzy, and partially occluded traffic sign targets at night and propose a high-precision nighttime traffic sign recognition method, "NTS-YOLO". The method firstly preprocessed the traffic sign dataset by adopting an unsupervised nighttime image enhancement method to improve the image quality under low-light conditions; secondly, it introduced the Convolutional Block Attention Module (CBAM) attentional mechanism, which focuses on the shape of the traffic sign by weighting the channel and spatial features inside the model and color to improve the perception under complex background and uneven illumination conditions; and finally, the Optimal Transport Assignment (OTA) loss function was adopted to optimize the accuracy of predicting the bounding box and thus improve the performance of the model by comparing the difference between two probability distributions, i.e., minimizing the difference. In order to evaluate the effectiveness of the method, 154 samples of typical traffic signs containing small targets and fuzzy and partially occluded traffic signs with different lighting conditions at nighttime were collected, and the data samples were subjected to the CBAM, OTA, and a combination of the two methods, respectively, and comparative experiments were conducted with the traditional YOLOv5 algorithm. The experimental results showed that "NTS-YOLO" achieved a significant performance improvement in nighttime traffic sign recognition, with a mean average accuracy improvement of 0.95% for the target detection of traffic signs and 0.17% for instance segmentation. [ABSTRACT FROM AUTHOR]

In Intelligent Transportation Systems (ITS), adaptive traffic control relies heavily on precise, real-time traffic data. Controllers use information such as vehicle count, vehicle density, traffic congestion, and intersection wait times to optimize traffic flow and improve efficiency. Traffic cameras collect and process this data, but environmental factors like rain can degrade the performance of data retrieval systems. We propose a vehicle detection method that integrates pixel area analysis with Deep Learning Super Sampling (DLSS) to enhance performance under rainy conditions. Our method achieved an accuracy of 80.95% under rainy conditions, outperforming traditional methods, and performing comparably to specialized methods such as DCGAN (93.57%) and DarkNet53 (87.54%). However, under extreme conditions such as thunderstorms, the method's accuracy dropped to 36.58%, highlighting the need for further improvements. These results, evaluated using the AAU RainSnow Traffic Surveillance Dataset, demonstrate that our method improves traffic data collection in diverse and challenging weather conditions while identifying areas for future research. [ABSTRACT FROM AUTHOR]

The detection of traffic signs in the natural scene requires a great deal of information due to the variations it undergoes over time for the output of the detection model to be effective and relevant. The amount of annotated data can range from a few hundred to thousands or even millions of examples, the availability of labeled data is often a critical bottleneck in the development and deployment of deep learning models, and acquiring high-quality annotations can be time-consuming and costly. Unlabeled data is easy to acquire but expensive to annotate. Several works focus on the use of active learning as an approach to get rid of the problem of costly annotation and computation time. To address these difficulties, we propose a Semi-Automatic Deep Image Annotation system using a new Evolutionary Strategy for Reinforcement Learning (SADIA-ESRL). Our experiments demonstrate remarkable efficiency on Natural Scene Traffic Sign and panel guide Arabic-Latin Text Dataset (NaSTSArLaT). The annotation approach studied has allowed for annotating only 1/4 of the images without compromising the model's efficiency. This reduction in annotation effort is accompanied by significant time savings, with the labeling process now taking as little as 1/5 of the initial time. Furthermore, this strategy grants us the capability to selectively annotate instances, ensuring optimal performance in the used detection model. [ABSTRACT FROM AUTHOR]

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets. [ABSTRACT FROM AUTHOR]

Nowadays, most car accidents occur due to driver fatigue, distraction, and sleepiness, and generally due to human error. Therefore, the importance of having cars with self-driving systems or advanced driver assistant systems has always been felt; because with the help of these systems, road traffic is managed by minimizing human intervention. One of the most essential features of such intelligent systems is the ability to recognize traffic signs. The paper introduces a novel framework for recognizing Iranian traffic signs, utilizing transfer learning and convolutional neural networks. Initially, a convolutional neural network is trained with the GTSRB dataset. Subsequently, this trained network is integrated as a feature-extracting block within a new network tailored for classifying Iranian traffic signs. The proposed model achieved an accuracy of 97.00% on the PTSD dataset. This framework represents the first instance of utilizing transfer learning for the detection and classification of Persian traffic signs. Additionally, this article pioneers the classification of Iranian traffic signs into 43 distinct classes, facilitated by the transfer learning method and the PTSD dataset. [ABSTRACT FROM AUTHOR]

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver's health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. [ABSTRACT FROM AUTHOR]

Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks. Different vision sensors offer varying advantages for detecting targets in changing environments and weather conditions. To analyze the benefits of both, corresponding LiDAR and camera data were collected at multiple roadside sites and then trained on separate semantic segmentation networks for object detection. A custom CNN architecture was built to handle highly asymmetric LiDAR data, and a network inspired by DeepLabV3+ was used for camera data. The performance of both networks was evaluated, and showed comparable accuracy. Inferences run on embedded platforms showed real-time execution matching the performance on the training hardware for edge deployments anywhere. Both camera and LiDAR semantic segmentation networks were successful in identifying vehicles of interest from the proposed viewpoint. These highly accurate vehicle detection networks can pair with a tracking mechanism to establish a non-intrusive roadside detection system. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSThe integration of digital towers in air traffic control (ATC) intensifies visual complexity of controllers, increasing the risk of detection failure (DF) to warnings and compromising airspace safety. The inherent variability in human situational awareness and behaviors further complicates the differentiation and recognition of various DFs. This study deciphers DF by categorizing it into types based on Endsley’s situation awareness theory, identifying specific causes and key indicators. A four-phase framework—DF classification, DF induction experiment, gaze dynamics analytics, and DF-type recognition—was applied to gaze data from 26 subjects. Results revealed distinct gaze patterns for non-perception, unaware perception, and aware perception of warnings, with continuous warnings weakening operators’ awareness but enhancing foresight of warning implications. A random forest model achieved 80% precision in DF-type recognition, offering empirical support for real-time DF recognition and targeted interventions to improve visual warning detection and human-computer interaction in aviation safety.Detection failure classification targets different situation awareness levels.Distinct gaze patterns mark different detection failure types.Random forest leads with 80% precision in a four-detection-type recognition task.Continuous warnings weaken the awareness of warnings but enhance projection. [ABSTRACT FROM AUTHOR]

In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low‐latency healthcare decision system using Q‐learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real‐time medical data processing while enforcing robust security throughout the IoT‐based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats. [ABSTRACT FROM AUTHOR]

In the process of vehicle detection in traffic monitoring, there are some problems such as vehicles shielding each other and insufficient distance target size, which leads to missing detection and false detection. To solve this problem, this study proposes a traffic vehicle detection algorithm based on YOLOv4 (You Only Look Once version 4) multi - scale fusion and attention mechanism. A new feature layer is added to YOLOv4's path aggregation network for multi - scale feature fusion to improve the model's ability to extract underlying texture features. The ECA (Efficient Channel Attention) channel attention module is embedded in front of YOLO Head detection head to reason- ably suppress and enhance the aggregated features. The CIoU (Complete Intersection over Union) loss function is replaced by the Soft CIoU loss function to improve the contribution of small target vehicles to the loss function. The experimental results on the publicly available vehicle data sets UA-DETRAC and KITTI show that compared to the original YOLOv4 algorithm, the average accuracy of the proposed algorithm improves by 2.45 percentage points and 1.14 percentage points, respectively, and the detection speed reaches 41.67 frame • s-1. The proposed algorithm performs well in detection accuracy when compared with other advanced algorithms. [ABSTRACT FROM AUTHOR]

To tackle the issues of low detection accuracy for small traffic signs in Advanced Driver-Assistance Systems (ADAS), we introduce an enhanced model RT-DETR_ASL to make ADAS more accurate and responsive. Firstly, we lighten and optimize the backbone network by substituting the Basic Block with an inverted residual block, thereby reducing the parameter count and enhancing computational speed. Secondly, we integrate a multi-scale deformable attention mechanism into the AIFI feature extraction network, augmenting the recognition and learning capabilities for small targets, which ultimately sharpens the precision of positioning and recognition. Additionally, to bolster the model's performance in detecting small, poorly defined traffic signs, we incorporate the S2 small-target detection layer to refine and strengthen the network's capabilities. During validation, when setting the GIoU (Generalized Intersection over Union) threshold at 0.7, the RT-DETR_ASL model demonstrated a 4.1% increase in mAP50 (mean Average Precision) over the baseline model. Upon further optimizing the loss function, the mAP value soared by an additional 4.51%, surpassing four other mainstream detection methods. Our experiments confirm that the RT-DETR_ASL model significantly enhances the detection accuracy of small traffic signs while maintaining real-time performance, contributing meaningfully to the advancement of autonomous driving assistance systems. It is hoped that the results of this research can make a valuable contribution to the further development of autonomous driving technology. [ABSTRACT FROM AUTHOR]

Road safety is a global concern, and vehicular accidents are unfortunately becoming more common. To address this issue, a Driver Assistant System using deep learning has been developed, aimed at reducing accidents by identifying traffic signs and alerting distracted drivers. The proposed system integrates Traffic Sign Detection, Classification, and a Voice Alert Module. Using You Only Look Once Version 4 (YOLO V4) for detection and a Convolutional Neural Network (CNN) for classification, it accurately recognizes signs. This system not only increases the safety of the driver and passengers, but also contributes to the overall safety of other road users. For training the Convolutional Neural Network Model and the YOLO v4 model, images from German Traffic Sign Recognition Benchmark (GTSRB) dataset are selected. To enhance the safety aspect of the system, a voice alert generation module is integrated. When a traffic sign is detected, the system generates voice alerts or warnings based on the recognized sign's meaning. These voice alerts are designed to guide and notify the driver about important traffic regulations or warnings, such as speed limits, stop signs and pedestrian crossings, promoting safer driving for all road users. [ABSTRACT FROM AUTHOR]

The network security knowledge base standardizes and integrates network security data, providing a reliable foundation for real-time network security protection solutions. However, current research on network security knowledge bases mainly focuses on their construction, while the potential to optimize intelligent security services for real-time network security protection requires further exploration. Therefore, how to effectively utilize the vast amount of historical knowledge in the field of network security and establish a feedback mechanism to update it in real time, thereby enhancing the detection capability of security services against malicious traffic, has become an important issue. Our contribution is fourfold. First, we design a feedback interface to update the knowledge base with information such as features of attack traffic, detection outcomes from network service functions (NSF), and system resource utilization. Second, we introduce a feature selection method that combines PageRank and RandomForest to identify influential features in the knowledge base and dynamically incorporate them into the NSFs. Third, we propose a path selection method that combines graph attention network (GAT) and deep reinforcement learning (DRL) to learn the local knowledge of the knowledge base and determine the optimal traffic path within the Service Function Chains (SFC). Finally, experimental results demonstrate that the knowledge base can be updated in real time according to feedback information, and the optimized service achieves an accuracy, recall, and F1 score exceeding 96%. Compared to preset paths and paths selected using the deep Q-network (DQN) method, our proposed method increases the malicious traffic detection rate by an average of 12.4% and 4.6%, respectively, enhances the total malicious traffic detection capability (TMTDC) of the path by 18.1% and 11.5%, and significantly reduces path detection delay. It has been verified that the proposed intelligent security optimization method can monitor malicious traffic in real time, update knowledge, and enhance the system's detection capability against malicious traffic. [ABSTRACT FROM AUTHOR]

Object detection and recognition algorithms are widely used in applications such as real-time monitoring and autonomous driving. However, there is limited research on traffic obstacle detection in complex scenarios involving road construction and sudden accidents. This gap results in low accuracy and difficulties in recognizing occluded targets, thereby hindering the further development and widespread adoption of intelligent transportation systems. To address these issues, this paper proposes an improved algorithm based on YOLOv7, incorporating a lightweight coordinate attention mechanism to focus on small objects at long distances and capture target location information. The use of a high receptive field enhances the feature hierarchy within the detection network. Additionally, we introduce the focal efficient intersection over union loss function to address sample imbalance, which accelerates the model's convergence speed, reduces loss values, and improves overall model stability. Our model achieved a detection accuracy of 98.1%, reflecting a 1.4% increase, while also enhancing detection speed and minimizing missed detections. These advancements significantly bolster the model's performance, demonstrating advantages for real-world applications. [ABSTRACT FROM AUTHOR]

The rapid development and use of artificial intelligence in various industries in recent years have markedly improved transportation systems. Automobile collisions can lead to numerous fatalities and significant financial losses. Automated vehicles can employ road detection as one of their functionalities. Notwithstanding the appalling nature of traffic accidents, numerous nations are employing artificial intelligence to create smart cities and autonomous vehicles. This research concentrates on traffic sign detection at night, building upon significant studies conducted by numerous researchers utilizing public road sign data sets. This dataset is essential for training autonomous vehicles to recognize traffic signs in low-light conditions. Nighttime object detection has numerous problems and is not less difficult than daytime detection. This research employs the YOLOv9 algorithm, a state-of-the-art, one-stage object detection model known for its speed and accuracy in identifying traffic signs during nighttime. The Contrast Limited Adaptive Histogram Equalization (CLAHE) method is evaluated and compared with nocturnal road sign detection. This study integrates YOLOv9 and CLAHE to provide an ideal model for enhancing nighttime road sign recognition efficiency. Our results indicate that the combination of YOLOv9 and CLAHE achieves the highest mean Average Precision (mAP) of 76.2%. The suggested model exhibits potential for incorporation into autonomous vehicle systems, facilitating real-time identification of road objects, pedestrians, and other vehicles, hence enhancing safety and navigation. [ABSTRACT FROM AUTHOR]

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and unauthorized access or tampering could lead to severe data breaches and integrity issues. However, as 5G networks extensively employ encryption technologies to protect data transmission, attackers can hide malicious content within encrypted communication, rendering traditional content-based traffic detection methods ineffective for identifying malicious encrypted traffic. To address this challenge, this paper proposes a malicious encrypted traffic detection method based on reconstructive domain adaptation and adversarial hybrid neural networks. The proposed method integrates generative adversarial networks with ResNet, ResNeXt, and DenseNet to construct an adversarial hybrid neural network, aiming to tackle the challenges of encrypted traffic detection. On this basis, a reconstructive domain adaptation module is introduced to reduce the distribution discrepancy between the source domain and the target domain, thereby enhancing cross-domain detection capabilities. By preprocessing traffic data from public datasets, the proposed method is capable of extracting deep features from encrypted traffic without the need for decryption. The generator utilizes the adversarial hybrid neural network module to generate realistic malicious encrypted traffic samples, while the discriminator achieves sample classification through high-dimensional feature extraction. Additionally, the domain classifier within the reconstructive domain adaptation module further improves the model's stability and generalization across different network environments and time periods. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of malicious encrypted traffic detection in 5G network environments, effectively enhancing the detection performance of malicious traffic in 5G networks. [ABSTRACT FROM AUTHOR]

Object detection is a major area of computer vision work, particularly for aerial surveillance and traffic control applications, where detecting vehicles from aerial images is essential. However, such images often lack semantic detail and struggle to identify small, densely packed objects accurately. This paper proposes improvements to the You Only Look Once version 5 (YOLOv5) model to enhance small object detection. Key modifications include adding a new prediction head with a 160×160 feature map, replacing the Sigmoid Linear Unit (SiLU) activation function with the Exponential Linear Unit (ELU), and swapping the Spatial Pyramid Pooling – Fast (SPPF) module with the Spatial Pyramid Pooling (SPP) module. The enhanced model was tested on two datasets: Dataset for Object Detection in Aerial Images (DOTA) v1.5 and CarJet, which focused on vehicle and plane detection. Results showed a 7.1% increase in mean Average Precision (mAP) on the DOTA dataset and a 2.3% improvement on the CarJet dataset, measured with an Intersection over Union (IoU) threshold of 0.5. These architectural changes to YOLOv5 notably improve small object detection accuracy, offering valuable potential for aerial surveillance and traffic control tasks. [ABSTRACT FROM AUTHOR]