A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

Vehicle detection plays a crucial role in the field of autonomous driving technology.However,directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection.Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes.This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network(FBPN).Firstly,to tackle challenges like vehicle occlusion and significant background interference,the efficient feature filtering module(EFFM)is introduced into the deep network,which amplifies the disparities between the features of the vehicle and the background.Secondly,the proposed global attention localization module(GALM)in the model neck effectively perceives the detailed position information of the target,improving both the accuracy and inference speed of themodel.Finally,the detection accuracy of small-scale vehicles is further enhanced through the utilization of a four-layer feature pyramid structure.Experimental results show that FBPN achieves an average precision of 60.8% and 97.8% on the BDD100K and KITTI datasets,respectively,with inference speeds reaching 344.83 frames/s and 357.14 frames/s.FBPN demonstrates its effectiveness and superiority by striking a balance between detection accuracy and inference speed,outperforming several state-of-the-art methods.

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

3D Vehicle Detection Algorithm Based onMultimodal Decision-Level Fusion

3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving.The algorithm based on the Camera-LiDAR object candidate fusion method(CLOCs)is currently considered to be a more effective decision-level fusion algorithm,but it does not fully utilize the extracted features of 3D and 2D.Therefore,we proposed a 3D vehicle detection algorithm based onmultimodal decision-level fusion.First,project the anchor point of the 3D detection bounding box into the 2D image,calculate the distance between 2D and 3D anchor points,and use this distance as a new fusion feature to enhance the feature redundancy of the network.Subsequently,add an attention module:squeeze-and-excitation networks,weight each feature channel to enhance the important features of the network,and suppress useless features.The experimental results show that the mean average precision of the algorithm in the KITTI dataset is 82.96%,which outperforms previous state-ofthe-art multimodal fusion-based methods,and the average accuracy in the Easy,Moderate and Hard evaluation indicators reaches 88.96%,82.60%,and 77.31%,respectively,which are higher compared to the original CLOCs model by 1.02%,2.29%,and 0.41%,respectively.Compared with the original CLOCs algorithm,our algorithm has higher accuracy and better performance in 3D vehicle detection.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

A Novel Ego Lanes Detection Method for Autonomous Vehicles

Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

A Light-weight Deep Neural Network for Vehicle Detection in Complex Tunnel Environments

With the rapid development of social economy,transportation has become faster and more efficient.As an important part of goods transportation,the safe maintenance of tunnel highways has become particularly important.The maintenance of tunnel roads has become more difficult due to problems such as sealing,narrowness and lack of light.Currently,target detection methods are advantageous in detecting tunnel vehicles in a timely manner through monitoring.Therefore,in order to prevent vehicle misdetection and missed detection in this complex environment,we propose aYOLOv5-Vehicle model based on the YOLOv5 network.This model is improved in three ways.Firstly,The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features,which reduces the number of model parameters;Next,all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation,and improve the detection speed of the model;Finally,to ensure the accuracy of the model,the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the model.Experiments results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.

Lightweight Intrusion Detection Using Reservoir Computing

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

A Vehicle Detection Method for Aerial Image Based on YOLO

With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.

Video Based Vehicle Detection and its Application in Intelligent Transportation Systems

Video based vehicle detection technology is an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and comprehensive vehicle behavior data collection capabilities. This paper proposes an efficient video based vehicle detection system based on Harris-Stephen corner detector algorithm. The algorithm was used to develop a stand alone vehicle detection and tracking system that determines vehicle counts and speeds at arterial roadways and freeways. The proposed video based vehicle detection system was developed to eliminate the need of complex calibration, robustness to contrasts variations, and better performance with low resolutions videos. The algorithm performance for accuracy in vehicle counts and speed was evaluated. The performance of the proposed system is equivalent or better compared to a commercial vehicle detection system. Using the developed vehicle detection and tracking system an advance warning intelligent transportation system was designed and implemented to alert commuters in advance of speed reductions and congestions at work zones and special events. The effectiveness of the advance warning system was evaluated and the impact discussed.

Multiobjective fault detection and isolation for flexible air-breathing hypersonic vehicle

An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects,variable operating conditions and possible failures of system components.A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors.To detect and isolate multiple actuator/sensor failures,a faulty linear parameter-varying（LPV） model of HSV is derived by converting actuator/system component faults into equivalent sensor faults.Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals.The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.

Patch-based vehicle logo detection with patch intensity and weight matrix

A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Boosted Vehicle Detection Using Local and Global Features

This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.

Misbehavior Detection Method by Time Series Change of Vehicle Position in Vehicle-to-Everything Communication

In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by CVs is on the basis of the assumption that correct vehicle information is shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method is suitable for detecting misbehavior in which the position information changes over time.