Vehicle Detection Research Articles

All-Day Vehicle Detection From Surveillance Videos Based on Illumination-Adjustable Generative Adversarial Network

All-Day Vehicle Detection From Surveillance Videos Based on Illumination-Adjustable Generative Adversarial Network

Investigating heterogeneity in the price elasticity of traffic flow in Melbourne: Evidence from traffic volume data

AbstractThis paper leverages a rich dataset of 1.24 billion traffic flow observations across 36,666 vehicle detectors to estimate the price elasticity of traffic flow with respect to fuel prices in Melbourne, Australia. I find that a 1% increase in fuel prices leads to a 0.0556% decrease in traffic flow across Melbourne, where traffic flow is defined as the number of vehicles passing a vehicle counter per hour. I find significant regional heterogeneity across Melbourne, with evidence of positive own‐price elasticities in regions in the centre of the city, and in peak commute times on both weekdays and weekends, which I argue is evidence of a congestion‐speed effect. Moreover, regions near the CBD with high incomes tend to be less elastic than regions further away with reduced access to public transport. These findings can be used to determine the optimal tax or charge to internalise the externalities produced by motor vehicles.

Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

SAFEPARK: Vehicle Detection and Traffic Violation Parking Management System

Abstract: The escalating motorcycle accident rates highlight the pressing need for improved safety measures. Helmets, a crucial safety gear, are often neglected, contributing significantly to fatalities. This paper addresses the pervasive issue of noncompliance with motorcycle safety rules, focusing on helmet usage and triple riding. Existing systems for monitoring lack precision, prompting our proposed Bike Traffic Violation System. Leveraging Haar Cascade and YOLOv3 models, it identifies motorcycles, detects riders without helmets, instances of triple riding, and even empty parking spots with unprecedented accuracy. The Machine Learning component employs a Support Vector Classification model, bolstered by 4-fold cross-validation, ensuring robustness. This innovative system provides real-time insights into traffic violations, enabling prompt interventions by law enforcement agencies. It overcomes manual identification shortcomings, offering a comprehensive solution for enforcement and awareness campaigns. In summary, our Bike Traffic Violation System not only advances automated traffic rule monitoring but introduces a novel methodology for precise detection, significantly contributing to enhanced road safety and accident prevention.

Traffic Management System Using IOT

Abstract: Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and causes major challenge to the movement of emergency vehicles. The reaction time required by the emergency responders plays a vital role to handle the situation. The greatest challenge they face is congestion of traffic flow. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the controlling of traffic congestion by IR sensors and detection of emergency vehicle using by RFID scanner method. The IR sensors is responsible for vehicle counting and RFID for the emergency vehicle. An IR sensors continuously count the passing vehicles & records the delay time between two vehicles. Flow of vehicles is normal means there is smooth traffic flow, or if in case, the passing time between two vehicles is decreased that means traffic congestion is happen ahead. In this situation traffic light turns to green, so that the emergency vehicle will reach its destination as fast as possible. The aim of this article is to identify and monitor the congestion system in order to provide efficient facilities. This journal also sets out a method that uses 433 MHZ RF Transmitter and receiver Wireless communication device technique and Internet of Things (IoT) to transmit the treatment request from the ambulance to the nearby hospitals and at the same time the LCD is installed on the road junction which displays the nearby hospital name and contact number for the ambulance, this smart traffic system which in turn changes the traffic signal cycle. This system can be implemented throughout the city thereby to reducing the delay.

Improving a method for detecting stealth aerial vehicles by using a network of two small-sized radars with decentralized information processing

The object of this study is the process of detecting stealth aerial vehicles by a network of two small-sized radars with decentralized signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth aerial vehicles with decentralized signal processing. The improved method for detecting a stealth aerial vehicle by a network of two small-sized radars with decentralized processing, unlike the known ones, provides for the following: – each radar emits its own probing signal; – each radar receives only its own signal; – coordinated filtering in the reception system of each radar of its signal; – quadratic detection of its signal in each radar; – finding the sum of detected signals in each radar at the output of its matched filter; – preliminary detection of the signal is carried out by each radar separately; – in each range element, the signal is compared with the threshold level; – when the threshold level in the range element is exceeded, such range element is assigned a value of one, otherwise – zero; – the sequence of zeros and ones obtained in this way in each radar of the network is transmitted to the central processing point; – at the central processing point, a decision is made about the presence or absence of a stealth aerial vehicle in the range element. Such a decision is made based on the results of the combined processing of binary sequences coming from the radars according to the "k out of m" criterion. It was established that when detecting a stealth aerial vehicle by a network of two small-sized radars, decentralized information processing provides a higher value of the conditional probability of correct detection, by (19–26) % on average

Detection and Classification of Vehicle in Traffic Video using DL Algorithm

Detection and Classification of Vehicle in Traffic Video using DL Algorithm

Driver Violation Detection for Motor Vehicle

Abstract: This paper introduces a comprehensive project aimed at significantly enhancing road safety through the development of an innovative AI- like device tailored for vehicles. This device represents a proactive approach to monitoring driver behavior in real-time, targeting a wide range of unsafe actions including overspeeding, seatbelt negligence, impaired driving, inappropriate overtaking, distracted driving, and drowsiness. By leveraging cutting-edge sensors and sophisticated algorithms, the device not only detects these risky behaviors but also issues timely warnings to drivers, thereby promoting greater awareness and compliance with safety regulations

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

Advanced Vehicle Detection and Tracking on Roads Using Deep Learning

Abstract: The world has been overtaken by artificial intelligence (AI), machine learning, and deep learning, which are affecting every aspect of our lives, including our need for companionship and the growth of smart cities and communities. Though tremendous progress has been made in fields like healthcare and education, transportation. Transportation is a significant barrier that has a huge financial and human cost on a worldwide scale. Traditional methods of assessing traffic on roads, which rely on inductive loops, don't give much information and can't control traffic in real-time. This study describes how the transportation sector could undergo a deep learning revolution. Deep learning techniques, including image and video analysis, have the potential to revolutionize road traffic monitoring by enabling computers to recognize traffic congestion, enhance road safety, and address various other concerns. The integration of deep learning technologies promises a new era of data-driven solutions for transportation-related issues, which could ultimately result in safer, more effective, and economically sustainable societies in the face of over a million annual fatalities and trillions of dollars in damages. In order to address these problems, this paper shows how well YOLOv3 detects automobiles and how well Darknet53 tracks these vehicles while they are on the road.

Multimodal contrastive learning for brain–machine fusion: From brain-in-the-loop modeling to brain-out-of-the-loop application

Harnessing the complementarity between the brain and artificial neural networks has shown great promise for the development of novel brain–machine fusion systems that may rival the robustness and flexibility of the human visual system. Nonetheless, due to the requirement of human involvement, brain–machine fusion models are challenging to apply in a brain-in-the-loop manner to handle task demands that involve long duration, high intensity and complex operating environments. In this paper, we propose a brain–machine fusion approach to achieve the brain-in-the-loop modeling and brain-out-of-the-loop application. The similarities and differences between the image representations of brain responses and image features computed by deep convolutional neural network (DCNN) are firstly analyzed using a rhesus monkey dataset, and the results lay the foundation for the feasibility of brain–machine fusion. A brain–machine fusion model is then developed and a multimodal supervised contrastive learning method is proposed to jointly learn the image representations for brain responses and DCNNs. The fusion model can be applied in a brain-out-of-the-loop manner, effectively addressing the challenges encountered by human-involved approaches. Extensive experiments on a self-built human vehicle detection dataset demonstrate the effectiveness of the proposed method in improving the generalization ability of the downstream image classifiers, both in cross-modal learning or multimodal fusion settings.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Vehicle Detection and Tracking Techniques Based on Deep Learning in Road Traffic Surveillance

This article discusses the application of deep learning in vehicle detection and tracking technology, elaborating on the basic concepts of deep learning and its advantages in vehicle target detection. Deep learning models such as Convolutional Neural Networks (CNNs) overcome the reliance on manual feature engineering by automatically learning image features. The article focuses on two deep learning detection frameworks, Faster R-CNN and YOLO. The former combines region proposal networks with region classification networks to achieve end-to-end optimization, while the latter transforms the detection task into a regression problem, enabling real-time detection in a single forward pass. Regarding vehicle tracking, the article explores the challenges of multi-object tracking such as occlusion, cross-movement, and the tracking requirements of different vehicle types. Deep learning applications in this field, such as the DeepSORT and Tracktor algorithms, combine CNNs, RNNs, and traditional tracking methods to achieve feature learning, historical state modeling, and probabilistic reasoning. Performance evaluation is discussed in terms of metrics like IoU, precision, recall, and F1 Score, comparing and analyzing the performance of different algorithms in vehicle detection and tracking tasks. Lastly, the article discusses the balance between real-time and accuracy in deep learning-based vehicle detection and tracking technology in road traffic monitoring, as well as its significant role in traffic accident warning and management.

Research on the Application of Recognition and Detection Technology in Automatic Driving

Object detection is the cornerstone of autonomous driving systems. In autonomous driving systems, vehicle detection is critical to maintaining traffic flow and avoiding collisions. The use of deep learning technology enables the system to skillfully and accurately identify vehicles on the road. Deep learning algorithms can provide autonomous vehicles with precise environmental awareness to enable informed driving decisions. Deep learning algorithms can achieve pedestrian detection. Such algorithms can improve the safety of vehicle operations. Through deep learning models, the system is able to identify and track pedestrians, ensuring appropriate responses in different traffic scenarios. The application of detection technology enables vehicles to promptly identify and interpret road signs and traffic lights in real-time. This recognition ability helps to comprehensively understand traffic conditions and improve the ability to adapt to different road conditions. This study provides an in-depth exploration of the importance and application of deep learning technology in the field of autonomous driving. Special attention is paid to the key role played by object detection methods. and propose future development directions. Deep learning algorithms can expand the application of learning technology to handle real-time traffic conditions and achieve rapid response. Achieve the fusion of multiple sensory information by improving the local processing capabilities of edge computing. This research aims to further improve the level of intelligence inherent in autonomous driving systems.

Bi-directional information guidance network for UAV vehicle detection

UAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

TRAFFIC PULSE-ROADWAY CONTROL SYSTEM

Rapid transit and transportation networks are essential to any country's economic progress. Long wait times, lost fuel, and financial losses are the outcomes of poor management and traffic congestion. For the sake of national development, a quick, affordable, and effective traffic control system is thus absolutely essential. In many nations, keeping an eye on and managing urban traffic is turning into a significant issue. The Traffic Monitoring Authority needs to come up with innovative ways to solve this issue because there are more and more cars on the road. Using automation and intelligent control techniques is one option to enhance the current transportation system's traffic flow and safety. Efficient and quick congestion relief is the aim. The project includes sound sensors that can recognize emergency cars as they approach in order to improve emergency vehicle detection. By prioritizing and streamlining emergency vehicle traffic at signalized junctions, this cutting-edge feature seeks to enhance overall traffic management and response times. Additionally, the concept has a novel element for pedestrian safety: when a signal changes from red to orange or orange to green, a dynamically triggered block is deployed. By erecting a barrier that guarantees pedestrians are securely clear of traffic before crossing, this creative solution seeks to improve pedestrian safety. Keywords: Traffic jams, traffic surveillance, automation, and clever control techniques, Changes in signals, sound sensors, emergency vehicle detection, quicker reaction times, barriers, and pedestrian safety.

REAL-TIME TRAFFIC LIGHT OPTIMIZATION USING AI AND IOT

In modern cities, the escalating number of vehicles has led to significant traffic issues, affecting road capacity and service quality. Traditional traffic control systems, reliant on fixed signal timers, struggle to adapt to changing traffic dynamics, exacerbating congestion. This paper proposes an innovative approach to traffic management utilizing advanced Computer Vision technology, specifically the YOLOv7 object detection algorithm. By analysing live CCTV footage at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real-time. The system architecture ensures swift processing and response times, with YOLOv7 enabling rapid and accurate vehicle detection. This enables the system to make timely decisions, thereby enhancing overall traffic management efficacy. Leveraging Artificial Intelligence (AI) and Machine Learning techniques, the proposed solution addresses the urgent need for innovative traffic management strategies in urban areas, aiming to alleviate congestion, enhance traffic flow, and reduce environmental impact. Hence, the integration of YOLOv7 technology with adaptive traffic signal switching algorithms represents a promising step towards addressing the complex challenges of urban traffic congestion. Key Words: Computer Vision, Artificial Intelligence, Machine Learning, Traffic Prediction, Adaptive Control, YOLO.

Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware

Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware

Optical fiber sensing and tensor AP clustering for high-speed road tunnel vehicle detection

Aiming at the problems of signal acquisition, feature extraction and vehicle recognition in highway tunnel vehicle detection, a new tunnel vehicle detection method is proposed by combining optical time-domain reflectometry distributed fiber sensing technology and tensor affine propagation clustering algorithm. Firstly, the distributed optical fiber system designed by optical time domain reflectometry was used to collect the running signals of tunnel vehicles and obtain the measurement data. Secondly, a high-order tensor sample set is constructed by using the spatial resolution of optical fiber as channel number and combining the feature number, time domain and frequency domain. Finally, tensor affine propagation clustering method and other clustering methods are used to test the accuracy. The test results show that the proposed method can better classify vehicles without destroying the original high-dimensional data structure. Meanwhile, the unsupervised clustering algorithm also reduces manual intervention in the identification process, increases the intelligence level of the whole vehicle detection model, and effectively improves the detection accuracy rate of tunnel vehicles.

A semi-supervised mixture model of visual language multitask for vehicle recognition

Effective vehicle detection is extremely vital in cities. However, existing vehicle recognition models exhibit limited functionality and have not been effectively integrated with language models to aid in vehicle monitoring within cities. To address these challenges, this paper proposes a semi-supervised mixture model with detection, segmentation, and question-answering capabilities, referred to as the Multi-task Visual Language Model (MTVLM), for vehicle recognition. Further, a vehicle dataset containing 8425 vehicle images from 13 different categories is introduced, which is collected across various urban scenarios and weather conditions. MTVLM consists of a detector, a language model, and a segmentation model. The detector YOLODS is based on an improved You Only Look Once algorithm (YOLOv4), which has a lower number of parameters and higher detection accuracy. The detection results of YOLODS are then used as prompts for the pre-trained language model, a distilled version of Bidirectional Encoder Representations from Transformers (DistilBERT), to answer questions related to the detected vehicles. Furthermore, the coordinates of bounding boxes obtained by YOLODS are input into the Segment Anything Model (SAM) as prompts to segment vehicles from images. Experimental results show that MTVLM achieves a mean average precision (mAP) of 97.12 % on the vehicle dataset, surpassing other state-of-the-art (SOTA) detectors. The high-precision detector also ensures highly accurate text prompts for input to DistilBERT and SAM. By employing text prompts, the potential and performance of the pre-trained models DistilBERT and SAM can be fully explored and utilized to better complete question-answering and segmentation tasks.