Vehicle Detection Approach Research Articles

An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV

The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

A Novel YOLO-ARIA Approach for Real-Time Vehicle Detection and Classification in Urban Traffic

A Novel YOLO-ARIA Approach for Real-Time Vehicle Detection and Classification in Urban Traffic

An Insight into Real Time Vehicle Detection and Classification Methods using ML/DL based Approach

Vehicle detection and classification is one of the major challenges for automated traffic surveillance as well as for military defence systems to identify enemy vehicles. For efficient real-time surveillance, accurate detection and classification of many vehicle kinds, including cars, trucks, buses, and so forth are important. This paper focuses on different methods of machine learning used toidentify thevehicle and then then categorize each vehicle according to the classes. It also explores the critical insight of existing vehicle detection and classification approaches using various machine learning methods like YOLO, CNN, R-CNN, and AdaBoost. The majority of the research projects now in existence solely consider increasing image-based accuracy, which has poor real-time performance and uses more computational resources.

Edge-Computing-Facilitated Nighttime Vehicle Detection Investigations With CLAHE-Enhanced Images

In this study, we propose a novel CLAHE-based nighttime image contrast enhancement approach for vehicle detection under nighttime conditions, which improves the contrast of low-quality nighttime images while preventing over-enhancement by employing the image dehazing technique. To implement and evaluate our proposed contrast enhancement method on nighttime images, we consider a scenario of using a camera-based Internet of Things (IoT)-edge computing device for traffic and road surveillance. Edge-computing and IoT technology enable significant amounts of novel studies to advance traffic system monitoring, sensing, control, and management. Considering multiple metrics of image enhancement quality, the proposed nighttime image contrast enhancement method outperforms some existing well-performing CLAHE-based methods. To provide accurate vehicle detection under nighttime conditions and different challenges, including vehicle overlapping, low-light conditions, camera vibrations, and image distortion, must be addressed. For this purpose, a deep neural network based on YOLOv5 architecture has been designed and trained using our custom-labeled dataset. The developed neural network is proven to be effective in the detection of different vehicles under low-light ambient conditions using video captured from a stationary camera. Experiments on our dataset show that the proposed contrast enhancement method greatly improves the detection performance of the trained YOLOv5 model under low-environment-light conditions compared with the model trained using unenhanced images. The model trained with enhanced images can provide an improvement of 5.7% on F1 score, 6.3% on mAP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{0.5,}$</tex-math> </inline-formula> and 3.4% on mAP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{0.5:0.95} $</tex-math> </inline-formula> under specific conditions.

Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.

Unleashing the power of generative adversarial networks: A novel machine learning approach for vehicle detection and localisation in the dark

AbstractMachine vision in low‐light conditions is a critical requirement for object detection in road transportation, particularly for assisted and autonomous driving scenarios. Existing vision‐based techniques are limited to daylight traffic scenarios due to their reliance on adequate lighting and high frame rates. This paper presents a novel approach to tackle this problem by investigating Vehicle Detection and Localisation (VDL) in extremely low‐light conditions by using a new machine learning model. Specifically, the proposed model employs two customised generative adversarial networks, based on Pix2PixGAN and CycleGAN, to enhance dark images for input into a YOLOv4‐based VDL algorithm. The model's performance is thoroughly analysed and compared against the prominent models. Our findings validate that the proposed model detects and localises vehicles accurately in extremely dark images, with an additional run‐time of approximately 11 ms and an accuracy improvement of 10%–50% compared to the other models. Moreover, our model demonstrates a 4%–8% increase in Intersection over Union (IoU) at a mean frame rate of 9 fps, which underscores its potential for broader applications in ubiquitous road‐object detection. The results demonstrate the significance of the proposed model as an early step to overcoming the challenges of low‐light vision in road‐object detection and autonomous driving, paving the way for safer and more efficient transportation systems.

A Review of Vehicle Detection Techniques for Intelligent Vehicles.

Robust and efficient vehicle detection is an important task of environment perception of intelligent vehicles, which directly affects the behavior decision-making and motion planning of intelligent vehicles. Due to the rapid development of sensor and computer technology, the algorithm and technology of vehicle detection have been updated rapidly. But, there are few reviews on vehicle detection of intelligent vehicles, especially covering all kinds of sensors and algorithms in recent years. This article presents a comprehensive review of vehicle detection approaches and their applications in intelligent vehicle systems to analyze the development of vehicle detection, with a specific focus on sensor types and algorithm classification. First, more than 300 research contributions are summarized in this review, including all kinds of vehicle detection sensors (machine vision, millimeter-wave radar, lidar, and multisensor fusion), and the performance of the classic and latest algorithms was compared in detail. Then, the application scenarios of vehicle detection with different sensors and algorithms were analyzed according to their performance and applicability. Moreover, we also systematically summarized the methods of vehicle detection in adverse weather. Finally, the remaining challenges and future research trends were analyzed according to the development of intelligent vehicle sensors and algorithms.

A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor.

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

Comparative Analysis of Deep Learning based Vehicle Detection Approaches

Numerous traffic-related problems arise as a result of the exponential growth in the number of vehicles on the road. Vehicle detection is important in many smart transportation applications, including transportation planning, transportation management, traffic signal automation, and autonomous driving. Many researchers have spent a lot of time and effort on it over the last few decades, and they have achieved a lot. In this paper, we compared the performances of major deep learning models: Faster RCNN, YOLOv3, YOLOv4, YOLOv5, and SSD for vehicle detection with variable image size using two different vehicle detection datasets: Highway dataset and MIOTCD. The datasets that are most commonly used in this domain are also analyzed and reviewed. Additionally, we haveemphasized the opportunities and challenges in this domain for the future.

Curve Path Prediction and Vehicle Detection in Lane Roads using Deep Learning for Autonomous Vehicles

The development of autonomous vehicles has seen significant progress in recent years. One of the key challenges in this field is the ability of the vehicle to accurately predict the path of the road and detect other vehicles in real-time. In this research paper, we propose a deep learning-based approach for curve path prediction and vehicle detection in lane roads for autonomous vehicles. The proposed approach utilizes convolutional neural networks to detect vehicles and predict their trajectory, taking into account road geometry and traffic conditions. The model will be trained on a large dataset of road scenes and tested in realistic simulations. The findings of this study will contribute to the advancement of autonomous vehicle technology, particularly in terms of improving the accuracy and reliability of curve path prediction and vehicle detection in lane roads.

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.

EnsembleNet: a hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models

EnsembleNet: a hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models

A Robust Framework for Object Detection in a Traffic Surveillance System

Object recognition is the technique of specifying the location of various objects in images or videos. There exist numerous algorithms for the recognition of objects such as R-CNN, Fast R-CNN, Faster R-CNN, HOG, R-FCN, SSD, SSP-net, SVM, CNN, YOLO, etc., based on the techniques of machine learning and deep learning. Although these models have been employed for various types of object detection applications, however, tiny object detection faces the challenge of low precision. It is essential to develop a lightweight and robust model for object detection that can detect tiny objects with high precision. In this study, we suggest an enhanced YOLOv2 (You Only Look Once version 2) algorithm for object detection, i.e., vehicle detection and recognition in surveillance videos. We modified the base network of the YOLOv2 by reducing the number of parameters and replacing it with DenseNet. We employed the DenseNet-201 technique for feature extraction in our improved model that extracts the most representative features from the images. Moreover, our proposed model is more compact due to the dense architecture of the base network. We utilized DenseNet-201 as a base network due to the direct connection among all layers, which helps to extract a valuable information from the very first layer and pass it to the final layer. The dataset gathered from the Kaggle and KITTI was used for the training of the proposed model, and we cross-validated the performance using MS COCO and Pascal VOC datasets. To assess the efficacy of the proposed model, we utilized extensive experimentation, which demonstrates that our algorithm beats existing vehicle detection approaches, with an average precision of 97.51%.

Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection

Camera-LiDAR fusion provides precise distance measurements and fine-grained textures, making it a promising option for 3D vehicle detection in autonomous driving scenarios. Previous camera-LiDAR based 3D vehicle detection approaches mainly focused on employing image-based pre-trained models to fetch semantic features. However, these methods may perform inferior to the LiDAR-based ones when lacking semantic segmentation labels in autonomous driving tasks. Motivated by this observation, we propose a novel semantic augmentation method, namely Sem-Aug, to guide high-confidence camera-LiDAR fusion feature generation and boost the performance of multimodal 3D vehicle detection. The key novelty of semantic augmentation lies in the 2D segmentation mask auto-labeling, which provides supervision for semantic segmentation sub-network to mitigate the poor generalization performance of camera-LiDAR fusion. Using semantic-augmentation-guided camera-LiDAR fusion features, Sem-Aug achieves remarkable performance on the representative autonomous driving KITTI dataset compared to both the LiDAR-based baseline and previous multimodal 3D vehicle detectors. Qualitative and quantitative experiments demonstrate that Sem-Aug provides significant improvements in challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hard</i> detection scenarios caused by occlusion and truncation.

Vehicle Detection Approach Adjusting Road Curves to Estimate Local Traffic Density under Real Driving Conditions

This paper has measured local traffic density under real driving conditions. The methodology employed is based on the traditional process of image recording. However, the conventional method has suffered from three typical limitations: long processing times, low vehicle detection performance, and inaccurate road length estimates. This study proposed two sequential approaches to resolve the three drawbacks. First, the you-only-look-once (YOLO) algorithm was used to shorten the computation time and enhance vehicle detection accuracy. Second, the vanishing point was identified during digital image processing to adjust the vertical and horizontal curves of roads, thereby calculating accurate road lengths. The proposed framework was applied to an arterial road in Seoul, South Korea. This approach was fast enough to measure local traffic density in real time. The performance of vehicle detection evaluated using a weighted mean average precision (mAP) was high at 88.73%. The accuracy of estimated road lengths evaluated using the Pearson correlation coefficient was also high at 0.97. Some co-benefits expected from the proposed method are also discussed.

A Portable Traffic Counting, Speed Estimation, and Classification Terminal Using IR-UWB Radar

Because of the stable performance under any light and weather conditions, radar becomes one of the most important sensors for traffic flow surveillance in intelligent transportation system (ITS). This paper proposes a novel portable traffic counting, speed estimation, and classification terminal using impulse radio ultra-wideband (IR-UWB) radar. The proposed terminal is lightweight, installed at the roadside, and potable. Moreover, it can only operate on a micro control unit of ARM Cortex-M7 with low-complexity signal processing approaches. Specifically, a vehicle detection and counting approach based on target location combined with trajectory tracking, a non-coherent speed estimation approach by exploiting the trajectory and prior geometric information, and a vehicle type classification approach based on four different attribute features are developed. Finally, experiment results show the improved performance of the proposed approaches.

A complementary and precise vehicle detection approach in RGB-T images via semi-supervised transfer learning and decision-level fusion

ABSTRACT Owing to the strong complementarity between visible and infra-red thermal (RGB-T) images, the two types of images receive the increased application of vehicle detection in traffic monitoring. Now, how to take full advantage of the two kinds of images for vehicle fusion detection has drawn wide publicity. Nevertheless, due to the requirement of high quality and high precision on vehicle fusion detection, and insufficiency of the infra-red thermal dataset, it is extremely hard to realize the vehicle fusion detection in RGB-T images. In our work, a complementary and precise vehicle fusion detection approach in RGB-T images is put forward, which combines the detection results of both visible and infra-red thermal images based on a decision-level fusion strategy. For vehicle detection in visible images, an effective and shallow network is constructed. For vehicle detection in infra-red thermal images, we do not directly train an effective network because of the insufficiency of the infra-red thermal vehicle samples. Instead, semi-supervised transfer learning is applied to combine the trained visible network for vehicle detection in infra-red thermal images. Next, for the decision-level fusion, we propose a novel fusion algorithm. It not only retains the vehicle results detected only in the visible images or only the infra-red thermal images but also fuses the detection results of the same vehicle in both visible and infra-red thermal images. Finally, VOT2019 and RGBT234 data sets are introduced to evaluate the proposed vehicle fusion detection approach. Compared with the main trend of current, it is indicated that the proposed approach can obtain competitive and superior vehicle fusion detection results in RGB-T images.

Peningkatan Akurasi Penghitungan Jumlah Kendaraan dengan Membangkitkan Urutan Identitas Deteksi Berbasis Yolov4 Deep Neural Networks

Models for vehicle detection, classification, and counting based on computer vision and artificial intelligence are constantly evolving. In this study, we present the Yolov4-based vehicle detection, classification, and counting model approach. The number of vehicles was calculated by generating the serial number of the identity of each vehicle. The object is detected and classified, marked by the display of bounding boxes, classes, and confidence scores. The system input is a video dataset that considers the camera position, light intensity, and vehicle traffic density. The method has counted the number of vehicles: cars, motorcycles, buses, and trucks. Evaluation of model performance is based on accuracy, precision, and total recall of the confusion matrix. The results of the dataset test and the calculation of the model performance parameters had obtained the best accuracy, precision. Total recall values when the model testing was carried out during the day where the camera position was at the height of 6 m and the loss of 500 was 83%, 93%, and 94%. Meanwhile, the lowest total accuracy, precision, and recall were obtained when the model was tested at night. The camera position was at the height of 1.5 m, and 900 losses were 68%, 77%, and 78%.