Vehicle Recognition Research Articles

UAV-Assisted Traffic Speed Prediction via Gray Relational Analysis and Deep Learning

Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is neglected. We develop a framework for traffic speed extraction and prediction based on UAV imagery processing, which consists of two parts: a traffic information extraction module based on UAV imagery recognition and a traffic speed prediction module based on deep learning. First, we use deep learning methods to automate the extraction of road information, implement vehicle recognition using convolutional neural networks and calculate the average speed of road sections based on panchromatic and multispectral image matching to construct a traffic prediction dataset. Then, we propose an attention-enhanced traffic speed prediction module that considers the spatiotemporal characteristics of traffic data and increases the weights of key roads by extracting important fine-grained spatiotemporal features twice to improve the prediction accuracy of the target roads. Finally, we validate the effectiveness of the proposed method on real data. Compared with the baseline algorithm, our algorithm achieves the best prediction performance regarding accuracy and stability.

SIMULATION RESULTS OF AVIATION SEARCH FOR GROUND OBJECTS USING RADAR AND THERMAL IMAGERS

The use on aircraft of various systems for remote sensing of the earth's surface leads to a significant difference in the resolutions achievable in the optical and radar (RL) wavelength ranges. The operating ranges of radar systems and thermal imaging (TI) systems differ significantly. This complicates their integration and requires the use of special algorithms to control the positions of the regions of interest on the underlying surface (SP). Such algorithms should maximize the width of the search bandwidth and ensure that there are no unscanned sections (“dead zones”) on it. This article presents the results of modeling that are useful in solving the problems of a reasonable choice of modern radar and TI systems, their parameters and estimates of the probabilistic ranges of characteristics in their complex application for operational monitoring of traffic. The results of modeling search-detection-recognition processes make it possible to predict the probabilistic-range characteristics of detection and recognition of vehicles under various backgrounds (grass, sand, asphalt, snow, etc.) for eighty percent of weather in middle latitudes. The results of modeling the processes of detection and recognition of objects by their RL and IR contrasts using synthetic aperture RL and modern arrays of sensitive elements in TI systems are presented.

STEP TOWARDS INTELLIGENT TRANSPORTATION SYSTEM WITH VEHICLE CLASSIFICATION AND RECOGNITION USING SPEEDED-UP ROBUST FEATURES

<p>Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.</p>

Research on the Intelligent Construction of UAV Knowledge Graph Based on Attentive Semantic Representation

Accurate target recognition of unmanned aerial vehicles (UAVs) in the intelligent warfare mode relies on a highly standardized UAV knowledge base, and thus it is crucial to construct a knowledge graph suitable for UAV multi-source information fusion. However, due to the lack of domain knowledge and the cumbersome and inefficient construction techniques, the intelligent construction approaches of knowledge graphs for UAVs are relatively backward. To this end, this paper proposes a framework for the construction and application of a standardized knowledge graph from large-scale UAV unstructured data. First, UAV concept classes and relations are defined to form specialized ontology, and UAV knowledge extraction triples are labeled. Then, a two-stage knowledge extraction model based on relational attention-based contextual semantic representation (UASR) is designed based on the characteristics of the UAV knowledge extraction corpus. The contextual semantic representation is then applied to the downstream task as a key feature through the Multilayer Perceptron (MLP) attention method, while the relation attention mechanism-based approach is used to calculate the relational-aware contextual representation in the subject–object entity extraction stage. Extensive experiments were carried out on the final annotated dataset, and the model F1 score reached 70.23%. Based on this, visual presentation is achieved based on the UAV knowledge graph, which lays the foundation for the back-end application of the UAV knowledge graph intelligent construction technology.

To Enhance Nighttime Vehicle Recognition with YOLO Technology Based on Optical and Thermal Images

To Enhance Nighttime Vehicle Recognition with YOLO Technology Based on Optical and Thermal Images

Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm

This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level becomes a problem that needs optimum investigation so that there are no errors in determining the type of vehicle. This study was conducted at mining sites to identify the class of vehicles that pass through the highway and how many types of vehicles pass through the road for vehicle recognition using a deep learning algorithm using several CNN models such as Yolo V4, Yolo V3, Densenet 201, CsResNext –Panet 50 and supported by the Darknet algorithm to support the training process. In this study, several experiments were carried out with other CNN models, but with peripherals and hardware devices, only 4 CNN models resulted in optimal accuracy. Based on the experimental results, the CSResNext-Panet 50 model has the highest accuracy and can detect 100% of the captured UAV video data, including the number of detected vehicle volumes, then Densenet and Yolo V4, which can detect up to 98% - 99%. This research needs to continue to be developed by knowing all classes affordable by UAV technology but must be supported by hardware and peripheral technology to support the training process.

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.

Object detection analysis based on Machine Learning Algorithms

The branch of artificial intelligence known as computer vision focuses on developing and utilizing digital systems to process, examine, and interpret visual data. Computer vision aims to make it possible for computing equipment to accurately recognise an object's digital image and perform the necessary action.The field of transportation and artificial intelligence includes vehicle discovery, which is pivotal. Intelligent transport system (ITS) is a fashion used to produce an effective road transportation system. The Automated Driving System which has made significant advancements in recent times, heavily relies on vehicle identification in its armature. The fashion of detecting vehicles on the road is used for objects similar as vehicle shadowing, counts, and average speed of each vehicle, business analysis, and vehicle categorization and can be applied in a variety of settings. A wide, active, and grueling field of computer vision is real- time object discovery. This recognizes semantic objects of a particular class in digital prints and pictures. For face recognition, vehicle recognition, rambler counting, web image analysis, security systems, and tone- driving motorcars, object discovery is constantly employed. Key Words: Vehicle dataset, ITS, Vehicle detection, Highway management

FedLVR: a federated learning-based fine-grained vehicle recognition scheme in intelligent traffic system

FedLVR: a federated learning-based fine-grained vehicle recognition scheme in intelligent traffic system

An efficient fine-grained vehicle recognition method based on part-level feature optimization

This paper presents an effective method for strengthening the discriminative ability of high-level deep features by enhancing and aggregating discriminative part-level features for the fine-grained vehicle recognition task. In general, the task of visual recognition concentrates more on the visual differences at the object level. However, for fine-grained object recognition, the visual differences between target objects typically exist in local discriminative areas, so it is more concerned about extracting fine-grained features from these part regions. In this context, we propose solving this issue with a novel feature extraction method from two perspectives: the generation of more feature descriptors of part regions through the learning process of deep networks and the aggregation of part-level discriminative features. This approach is designed to improve the backbone networks to generate finer-level part features through a part-level feature enhancement module and to investigate the intrinsic part-level features of the backbone networks with the help of a feature aggregation module. The enhancement module efficiently finds the finer features highly correlated to the part regions. Then the feature aggregation module builds correlations of similar part features through feature grouping and fusion. Moreover, our proposed method does not require additional parts annotations and achieves comparable performance on two widely-used benchmarks for recognizing fine-grained vehicle types. Experimental results and explainable visualizations demonstrate the effectiveness of the proposed method.

A Vehicle Recognition Model Based on Improved YOLOv5

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm’s backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower.

Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers

Controlling and managing city traffic is one of them. In order to use image processing to prevent accidents on the road, vehicle tracking and detection are essential. By following moving objects, surveillance video monitoring and human activity recording are carried out. By taking this into account, a useful technique for image processing that detects automobiles from the image is suggested. For numerous vehicle tracking and detection systems, the ECNN-SVM (Enhanced Convolution Neural Network with Support Vector Machine) has just been introduced. However, the larger dimensional data space and inaccurate edge recognition make this system’s performance difficult. The WHOSVD (Weight High Order Singular Value Decomposition) approach, which reduces the dimension and breaks up the positive and negative training picture samples, is established to improve training speed and visual vehicle recognition. To effectively identify the edges at corners, improved canny edge detection is used for edge detection. Mean Kernel Fuzzy C Means (MKFCM) clustering algorithm-based three-dimensional bounding box estimation is used to identify the vehicle items. By merging the feature value of samples with their class labels, the Speed Factor Based Cuckoo Search Algorithm (SFCSA) is introduced for feature selection. The WHOSVD algorithm was used as the input for the enhanced convolutional neural network (ECNN), which is introduced for low-dimensional space and is used for vehicle detection and tracking. Occlusion problems are resolved and target features are further identified using a machine learning classifier. For common algorithms like CNN+SVM, Support Vector Machine (SVM), and the proposed technique, experimentation is done in regards to the metrics of accuracy, f-measure, precision, and recall for performance evaluation.

Vehicle Recognition Based on Region Growth of Relative Tension and Similarity Measurement of Side Projection Profile of Vehicle Body

Current vehicle recognition methods are less concerned simultaneously with: (1) the scale difference between vehicles and other objects in urban city; and (2) the impact of physical characteristics of vehicles. Based on the region growth of relative tension, a method for measuring the similarity of side projection profile of a vehicle’s body is proposed for recognizing vehicles. First, region growth of relative tension is used to divide 3D point clouds into a series of spatial regions. Point clouds in these regions are projected to a 2D plane. Then, relevant 2D features are extracted, including side projection profile of vehicle body and sizes of vehicles. Screening by these relevant features, part of these regions, and point clouds inside them which conforms to the similarity measurement conditions of vehicles are selected. Quantitative evaluations on the selected data set show that the proposed algorithm achieves a recall, precision, and F-score of 0.837, 0.979, and 0.902, respectively, in recognizing vehicles. Comparative studies demonstrate the superior performance of the proposed algorithm.

Vehicle Recognition Based on Carrier-Free UWB Radars Using Contrastive Multi-View Learning

A novel carrier-free ultrawideband (UWB) radar-based automatic vehicle recognition system is reported. We provide complete transceiver units, design a UWB Vivaldi antenna with centimeter-scale dimensions and bandwidth from 0.89 to 5.02 GHz, and propose the multi-view-based contrastive learning algorithm for target recognition. Simulated results of the antenna are compared with measured results and are shown to be in good agreement. Though target recognition benefits from large, curated labeled data, its application to problems with limited annotated data remains a challenge. In addition, the target-aspect sensitive issue also impacts the performance of models. For that, we combine contrastive learning with unlabeled multi-view data to learn target-aspect-invariant representations. The experiments on the different datasets demonstrate the effectiveness and generalization of our method.

A Car Target Detection Method based on YOLOv7-tiny

If the driver can discover the safety hazards during the driving and brake the vehicle in time, it will effectively reduce the occurrence of traffic accidents and reduce losses. In order to accurately and quickly detect the vehicle in front, this article proposes a car target detection method based on the YOLOv7-tiny algorithm. This method can use the machine vision to detect the vehicle in front. Provide security assistance. The experimental results show that the average accuracy rate of the methods proposed in this article on six common vehicle recognition reached 80.8%, and the model is more lighter.

Vehicle Feature Recognition via a Convolutional Neural Network with an Improved Bird Swarm Algorithm

<p>Accurate vehicle feature recognition is an important element in traffic intelligence systems. To address the problems of slow convergence and weak generalization ability in using convolutional neural networks to improve vehicle feature recognition, we propose an improved bird swarm algorithm to optimize convolutional neural networks (IBSA-CNNs) for vehicle recognition strategies. First, we use the center of gravity backward learning strategy and similarity- and aggregation-based optimization strategy in population initialization and foraging behavior, respectively, to improve the algorithm performance and avoid falling into a local optimum. Second, the improved bird swarm algorithm is used to optimize the weights of the convolutional and pooling layers of the convolutional neural network to improve the neural network performance. Finally, we tested the performance of the improved bird swarm algorithm in simulation experiments using benchmark functions. The recognition performance of IBSA-CNN was tested by the UCI dataset, and in the traffic vehicle dataset BIT-Vehicle, it improved 4.9% and 6.8% compared with R-CNN and CNN, respectively, indicating that IBSA-CNN has better vehicle feature recognition.</p> <p> </p>

Vehicle Object Detection Based on Deep Learning

With the continuous improvement of science and technology and living standards, cars have become a necessary means of transportation for people to travel, which is bound to be followed by traffic accidents, so it is particularly necessary to detect or identify cars. Aiming at the above problems, this paper focuses on the application of deep learning in vehicle recognition problems, and is committed to finding a vehicle recognition algorithm with high recognition rate and stability. This paper focuses on the analysis of SSD algorithm and its basic theory convolutional neural network. Finally, this paper uses the pictures of stationary vehicles and a film video respectively to identify the stationary and moving state of vehicles. In the process of image recognition, the rationality and accuracy of the proposed method are verified according to the accuracy rate given above the image.

A simulation method on target strength and circular SAS imaging of X-rudder UUV including multiple acoustic scattering

Target strength (TS) and circular synthetic aperture sonar (CSAS) images provide essential information for active acoustic detection and recognition of non-cooperative unmanned undersea vehicles (UUVs), which pose a significant threat to underwater preset facilities. To access them, we propose an iterative physical acoustics (IPA)-based method to simulate the multiple acoustic scattered fields on rigid surfaces in high-frequency cases. It uses the Helmholtz integral equation with an appropriate Green's function in terms of the Neumann series, and then incorporates the ideas of triangulation and iteration into a numerical implementation. Then two approximate analytic formulae with precise physical meanings are derived to predict the TS and CSAS images of concave targets, respectively. There are no restrictions on the surface's curvature and the order of multiple scattering. The method is validated against the finite element method (FEM) for acoustic scattering from a sphere segment and against an experiment involving an X-rudder UUV's stern. On this basis, we simulate and analyze the TS and CSAS images of an X-rudder UUV. In addition, the influence of the angle of adjacent rudders on the multiple scattering characteristics is discussed. Results show that this method can potentially predict accurate UUV features, especially the multiple scattered features.

Real-Time Image Processing Using Deep Learning With Opencv And Python

The observation of laptop imaginative and prescient aids in the improvement of techniques for figuring out presentations and pictures. It contains a variety of functions, including picture recognition, object identification and image production among others. Face recognition, vehicle recognition, online photos, and safety systems all employ object detection. The goal is to identify things using the You Only Look Once (YOLO) technique. When compared to previous object identification algorithms, our method focuses on a few key areas. Unlike other algorithms, YOLO scans the whole photograph through estimating bounding containers the use of convolutional networks and sophistication possibilities for those containers. This permits YOLO to understand an photograph extra fast than different algorithms together with convolutional neural networks and speedy convolutional neural network. By using dependencies like OpenCV, we can identify each object in an image based on the region object in a distinct rectangular box, identify every item and assign its tag to the item the use of those strategies and algorithms primarily based totally on deep learning, which is likewise primarily based totally on system learning. It moreover consists of the nuances of every item-marking strategy.

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization.

Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.