Vehicle Detection Accuracy Research Articles

Sec-CLOCs: Multimodal Back-End Fusion-Based Object Detection Algorithm in Snowy Scenes

LiDAR and cameras, often regarded as the “eyes” of intelligent driving vehicles, are vulnerable to adverse weather conditions like haze, rain, and snow, compromising driving safety. In order to solve this problem and enhance the environmental sensing capability under severe weather conditions, this paper proposes a multimodal back-end fusion object detection method, Sec-CLOCs, which is specifically optimized for vehicle detection under heavy snow. This method achieves object detection by integrating an improved YOLOv8s 2D detector with a SECOND 3D detector. First, the quality of image data is enhanced through the Two-stage Knowledge Learning and Multi-contrastive Regularization (TKLMR) image processing algorithm. Additionally, the DyHead detection head and Wise-IOU loss function are introduced to optimize YOLOv8s and improve 2D detection performance.The LIDROR algorithm preprocesses point cloud data for the SECOND detector, yielding 3D object detection results. The CLOCs back-end fusion algorithm is then employed to merge the 2D and 3D detection outcomes, thereby enhancing overall object detection capabilities. The experimental results show that the Sec-CLOCs algorithm achieves a vehicle detection accuracy of 82.34% in moderate mode (30–100 m) and 81.76% in hard mode (more than 100 m) under heavy snowfall, which demonstrates the algorithm’s high detection performance and robustness.

Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR

High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the over-segmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m2, respectively. The trajectory extraction precision outperforms most of the state-of-the-art algorithms.

Differential Analysis of Emergency Vehicle Detection in Urban Traffic : A Systematic Review

The differential analysis of emergency vehicle detection in urban traffic is crucial for improving response times and reducing traffic-related delays during emergencies. This systematic review aims to analyze various methods and technologies, such as acoustic, visual, and sensor-based systems, used for detecting emergency vehicles in complex urban environments. The motivation behind this study is the growing need for more efficient traffic management systems that prioritize emergency vehicles, minimizing delays caused by congestion. However, limitations include the inconsistent performance of detection systems in varying weather, lighting, and noise conditions, as well as integration challenges with existing infrastructure. The objective is to evaluate current detection methods, identify their limitations, and propose potential improvements to enhance the accuracy and reliability of emergency vehicle detection in urban traffic systems.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Multi-sensor feature fusion for vehicle detection based on the fuzzy longest common subsequence

To mitigate the impact of sensor misdetection and omission on the multi-sensor feature fusion algorithm, this paper proposes a vehicle detection method for multi-sensor feature fusion based on the fuzzy longest common subsequence algorithm. Firstly, the lightweight YOLOv4 algorithm is utilized to obtain image features. Secondly., an adaptive threshold-based clustering method processes point cloud data, extracting trajectory information from both image and point cloud sources. Then, an enhanced longest common subsequence algorithm, incorporating a fuzzy membership function, is introduced to assess the similarity between the lidar target trajectory and the camera target trajectory. Next, the fusion of lidar and camera detection results is carried out based on the computed similarity scores. Ultimately, experimental validation affirms that this algorithm significantly improves vehicle detection accuracy. This enhancement contributes to a more robust and reliable vehicle environment sensing system.

Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception

Robust roadside traffic perception requires integrating the strengths of multi-source sensors under various adverse conditions, which is challenging but indispensable for formulating effective traffic management strategies. One limitation of existing radar-camera perception systems is that they focus on integrating multi-source information without directly considering scene information, leading to difficulties in achieving scene adaptive fusion. How to establish the connection between scene information and multi-source information is the key challenge to solving this problem. In this article, we propose a Scene adaptive Sensor Fusion (SSF) framework that characterizes scene information and integrates it into radar-camera fusion schemes, aiming to achieve high-quality roadside traffic perception. Specifically, we introduce a multi-source object association method that accurately associates multi-source sensor information on the roadside. We then utilize coding techniques to characterize the scene information, including visibility characterization regarding lighting and weather conditions, and road characterization regarding sensor viewpoint. By incorporating sensor and scene information into the fusion model, the SSF framework effectively establishes the connection between them. We evaluate the SSF framework on the Roadside Radar and Video Dataset (RRVD) and the Traffic flow Parameter Estimation Dataset (TPED), both collected from real-world traffic scenarios. Experiments demonstrate that SSF significantly improves vehicle detection accuracy under various adverse conditions compared to traditional single-source sensing methods and other state-of-the-art fusion techniques. Furthermore, vehicle trajectories based on SSF detection results enable accurate traffic parameter estimation, such as volume, speed, and density, in complex and dynamic environments.

Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems.

YOLO-Based Vehicle Detection: Literature Review

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.

Improved vehicle detection: unveiling the potential of modified YOLOv5

The detection of vehicles is a crucial task in various applications. In recent years, the quantity of vehicles on the road has been rapidly increasing, resulting in the challenge of efficient traffic management. To address this, the study introduces a model of enhancing the accuracy of vehicle detection using a proposed improved version of the popular You Only Look Once (YOLO) model, known as YOLOv5. The accuracy of vehicle detection using both the original versions of YOLOv5 and our proposed YOLOv5 algorithm has been evaluated. The evaluation is based on key accuracy metrics such as precision, recall, and mean Average Precision (mAP) at an Intersection over Union (IoU). The study's experimental results show that the original YOLOv5 model achieved a mean Average Precision (mAP) of 61.4% and the proposed model achieved an mAP of 67.4%, outperforming the original by 6%. The performance of the proposed model was improved based on the architectural modifications, which involved adding an extra layer to the backbone. The results reveal the potential of our proposed YOLOv5 for real-world applications such as autonomous driving and traffic monitoring and may involve further fine-tuning, robotics and security system and exploring broader object detection domains.

DV3-IBi_YOLOv5s: A Lightweight Backbone Network and Multiscale Neck Network Vehicle Detection Algorithm.

Vehicle detection is a research direction in the field of target detection and is widely used in intelligent transportation, automatic driving, urban planning, and other fields. To balance the high-speed advantage of lightweight networks and the high-precision advantage of multiscale networks, a vehicle detection algorithm based on a lightweight backbone network and a multiscale neck network is proposed. The mobile NetV3 lightweight network based on deep separable convolution is used as the backbone network to improve the speed of vehicle detection. The icbam attention mechanism module is used to strengthen the processing of the vehicle feature information detected by the backbone network to enrich the input information of the neck network. The bifpn and icbam attention mechanism modules are integrated into the neck network to improve the detection accuracy of vehicles of different sizes and categories. A vehicle detection experiment on the Ua-Detrac dataset verifies that the proposed algorithm can effectively balance vehicle detection accuracy and speed. The detection accuracy is 71.19%, the number of parameters is 3.8 MB, and the detection speed is 120.02 fps, which meets the actual requirements of the parameter quantity, detection speed, and accuracy of the vehicle detection algorithm embedded in the mobile device.

Vehicle detection algorithm for foggy based on improved AOD-Net

To strengthen the safety monitoring of foggy road traffic and maintain the safety of vehicle driving on foggy roads, image dehazing algorithms are used to improve the clarity of road images detected in foggy environments, thereby improving the detection ability and monitoring efficiency of intelligent transportation systems for vehicle targets. Due to the low accuracy of vehicle detection and serious problem of missed detections in haze environments, this paper proposes an improved All-in-One Dehazing Network (AOD-Net) algorithm for detecting foggy vehicles, which adds batch normalization (BN) layers after each layer of convolution in AOD-Net, accelerating the convergence of the model and controlling overfitting. To enhance image detail information, an effective pyramid-shaped PSA attention module is embedded to extract richer feature information, enrich model representation, and improve the loss function to a multi-scale structural similarity (MS-SSIM) + L1 mixed loss function, thereby improving the quality, brightness, and contrast of dehazing images. Compared with current image dehazing algorithms, the dehazing quality of our algorithm is superior to other dehazing algorithms, such as dark channel prior (DCP), Dehaze-Net, and Fusion Feature Attention Network (FFA-Net). Compared with AOD-Net, the improved algorithm has increased the peak signal-to-noise ratio by 3.23 dB. At the same time, after the improved AOD-Net image dehazing processing, YOLOv7 object detection was performed and experimentally validated on a real foggy dataset. The results showed that compared with the previous method, it had better recognition performance in foggy detection and recognition, and higher detection accuracy for vehicles.

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.

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.

Vision based intelligent traffic light management system using Faster R‐CNN

AbstractTransportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

A deep neural network for vehicle detection in aerial images

This research paper highlights the significance of vehicle detection in aerial images for surveillance systems, focusing on deep learning methods that outperform traditional approaches. However, the challenge of high computation complexity due to diverse vehicle appearances persists. The motivation behind this study is to highlight the crucial role of vehicle detection in aerial images for surveillance systems, emphasizing the superior performance of deep learning methods compared to traditional approaches. To address this, a lightweight deep neural network-based model is developed, striking a balance between accuracy and efficiency enabling real-time operation. The model is trained and evaluated on a standardized dataset, with extensive experiments demonstrating its ability to achieve accurate vehicle detection with significantly reduced computation costs, offering a practical solution for real-world aerial surveillance scenarios.

Aero-YOLO: An Efficient Vehicle and Pedestrian Detection Algorithm Based on Unmanned Aerial Imagery

The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including limited payload capacity, resulting in insufficient computing power, low recognition accuracy due to small target sizes in images, and missed detections caused by dense target arrangements. To address these challenges, this study proposes a lightweight UAV image target detection method based on YOLOv8, named Aero-YOLO. The specific approach involves replacing the original Conv module with GSConv and substituting the C2f module with C3 to reduce model parameters, extend the receptive field, and enhance computational efficiency. Furthermore, the introduction of the CoordAtt and shuffle attention mechanisms enhances feature extraction, which is particularly beneficial for detecting small vehicles from a UAV perspective. Lastly, three new parameter specifications for YOLOv8 are proposed to meet the requirements of different application scenarios. Experimental evaluations were conducted on the UAV-ROD and VisDrone2019 datasets. The results demonstrate that the algorithm proposed in this study improves the accuracy and speed of vehicle and pedestrian detection, exhibiting robust performance across various angles, heights, and imaging conditions.

Vehicle Multi-Object Detection and Tracking Algorithm Based on Improved You Only Look Once 5s Version and DeepSORT

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads.

A Multi-Task Network Based on Dual-Neck Structure for Autonomous Driving Perception.

A vision-based autonomous driving perception system necessitates the accomplishment of a suite of tasks, including vehicle detection, drivable area segmentation, and lane line segmentation. In light of the limited computational resources available, multi-task learning has emerged as the preeminent methodology for crafting such systems. In this article, we introduce a highly efficient end-to-end multi-task learning model that showcases promising performance on all fronts. Our approach entails the development of a reliable feature extraction network by introducing a feature extraction module called C2SPD. Moreover, to account for the disparities among various tasks, we propose a dual-neck architecture. Finally, we present an optimized design for the decoders of each task. Our model evinces strong performance on the demanding BDD100K dataset, attaining remarkable accuracy (Acc) in vehicle detection and superior precision in drivable area segmentation (mIoU). In addition, this is the first work that can process these three visual perception tasks simultaneously in real time on an embedded device Atlas 200I A2 and maintain excellent accuracy.

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.