Vehicle Detection Techniques Research Articles

Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions.

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.

Enhanced real-time road-vehicles’ detection and tracking for driving assistance

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

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.

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.

Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification

The fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Recent advancements have demonstrated the efficacy of DL models like convolutional neural networks (CNN) in RSI classification tasks. In this aspect, this study proposes a novel honey badger optimization algorithm with an ensemble learning-based vehicle detection and classification (HBOAEL-VDC) technique. The purpose of the study is to design ensemble DL models for accurate vehicle identification and classification processes. To accomplish this, the HBOAEL-VDC technique makes use of an improved RetinaNet model for the detection of objects, i.e., vehicles on the RSI. Moreover, the classification of detected vehicles takes place using the ensemble learning process, comprising three DL models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). Furthermore, the HBOA-based parameter tuning process gets carried out to adjust the hyperparameter values of the DL models and thereby improve the classification results. The simulation outcome of the HBOAEL-VDC approach is tested on benchmark RSI databases. The experimentation outcomes reported the enhanced vehicle classification performance of the HBOAEL-VDC approach over other recent DL models.

Finding and Tracking Automobiles on Roads for Self-Driving Car Systems

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively.

Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation

ABSTRACT Nowadays, deploying an intelligent vehicle detection system (IVDS) in diverse traffic is a work priority. It provides real-time traffic information with vehicle counts and types of vehicles. IVDS deployment in diverse traffic is challenging because different vehicle classes occlude each other on the road. In recent years, convolutional neural network (CNN) based deep learning (DL) methods have attained incredible progress in implementing IVDS. However, most CNN-based DL methods do not include diverse traffic conditions in Asian countries. Also, due to existing feature extraction backbones, they cannot accurately detect multi-scale vehicles. This work proposes an advanced visual computing deep learning (AVCDL) method with a vast labeled vehicle dataset to detect vehicles in diverse traffic. It includes an ensemble backbone and an improved multi-stage vehicle detection head (MSVDH). An ensemble CNN backbone extracts the vehicle features and combines them on a single channel via a feature concatenation. The final detection is carried out by an improved MSVDH that classifies the target vehicles. The proposed method is examined, tested, and evaluated using traffic statistics. It is contrasted with current cutting-edge vehicle detection techniques. It achieves 86.32% mean average precision (mAP) on self-collected diverse traffic labeled dataset (DTLD) and 86.17% mAP on KITTI. Moreover, the real-time performance is validated with NVIDIA Jetson Tx2 and Nano boards. It achieves 15 frames per second (FPS) on Jetson Tx2 and 7 FPS on Jetson Nano.

Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

SummarySeveral autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi‐scale dense nested deep CNN (MSDN‐DCNN) and regional search grasshopper optimization algorithm (RS‐GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre‐processed, which includes frame conversion, redundancy removal, and image enhancement. The pre‐processed frames are given as input to the MSDN‐DCNN for multi‐scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS‐GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN‐RS‐GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.

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.

Robust Vehicle Detection Based on Improved You Look Only Once

Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

Spotted hyena optimizer with deep learning enabled vehicle counting and classification model for intelligent transportation systems

<abstract> <p>Traffic surveillance systems are utilized to collect and monitor the traffic condition data of the road networks. This data plays a crucial role in a variety of applications of the Intelligent Transportation Systems (ITSs). In traffic surveillance, it is challenging to achieve accurate vehicle detection and count the vehicles from traffic videos. The most notable difficulties include real-time system operations for precise classification, identification of the vehicles' location in traffic flows and functioning around total occlusions that hamper the vehicle tracking process. Conventional video-related vehicle detection techniques such as optical flow, background subtraction and frame difference have certain limitations in terms of efficiency or accuracy. Therefore, the current study proposes to design the spotted hyena optimizer with deep learning-enabled vehicle counting and classification (SHODL-VCC) model for the ITSs. The aim of the proposed SHODL-VCC technique lies in accurate counting and classification of the vehicles in traffic surveillance. To achieve this, the proposed SHODL-VCC technique follows a two-stage process that includes vehicle detection and vehicle classification. Primarily, the presented SHODL-VCC technique employs the RetinaNet object detector to identify the vehicles. Next, the detected vehicles are classified into different class labels using the deep wavelet auto-encoder model. To enhance the vehicle detection performance, the spotted hyena optimizer algorithm is exploited as a hyperparameter optimizer, which considerably enhances the vehicle detection rate. The proposed SHODL-VCC technique was experimentally validated using different databases. The comparative outcomes demonstrate the promising vehicle classification performance of the SHODL-VCC technique in comparison with recent deep learning approaches.</p> </abstract>

Classification and Counting of Vehicle using Image Processing Techniques

Abstract: In this research work, we explore the vehicle detection technique that can be used for traffic surveillance systems. This system works with the integration of CCTV cameras for detecting the vehicle. Vehicle Tracking and Counting Monitoring highway traffic is an important application of computer vision research. In this paper, we analyze congested highway situations where it is difficult to track individual vehicles in heavy traffic because vehicles either occlude each other. In this we focus on the issues to propose a viable solution and we apply the vehicle detection results to multi object tracking and vehicle counting. Vehicle detection and vehicle type recognition is a practical application of machine learning concepts and is directly applicable for various operations in a traffic surveillance system contributing to an intelligent traffic surveillance system. This paper will introduce the processing of automatic vehicle detection and recognition using static image datasets. Further using the same technique, we shall improvise vehicle detection by using live CCTV surveillance. The surveillance system includes detection of moving vehicles and recognizing them, counting number of vehicles.

Vehicle detection and classification with spatio-temporal information obtained from CNN

Vehicle detection and classification with convolutional neural networks (CNN) is encouraging and fascinating for intelligent transportation applications. In general, CNN is trained with huge labeled vehicle images, which are rare to find in developing country scenarios. This work proposes a vehicle detection technique with CNN that does not require any labeled vehicle dataset. A CNN is trained with road marks and these act as the background. When a vehicle occupies a road mark a logic 1 is recorded else 0 is saved in the database. This accumulated occupancy data is spatio-temporal information from which vehicle width and length are approximated and classified. This method does not require any GPU for training as well as for implementation in real-time. The detection accuracy of the proposed method on real-time videos is about 98.5%. The runtime performance of CNN model on NVIDIA Jetson Tx2 is 0.01 s, whereas on RaspberryPi4 is 0.03 s. The given method even works in shadow and low-light conditions.

Performance Evaluation and Analysis of Drone-Based Vehicle Detection Techniques From Deep Learning Perspective

From smart cities development perspective, road vehicle detection exploiting drone-based aerial imagery is a crucial part of traffic surveillance and monitoring systems where effective results are of utmost demand. A recent boom in the field of deep learning (DL) has provided remarkable development in the problem of vehicle detection. Aerial views pose more complexity with respect to the ground view but the rapid advancement in the field of DL, the volume of data, and hardware configuration has facilitated the realization of these intelligent detection systems effectively. In this article, a detailed performance evaluation of some of the main state-of-the-art DL-based object detection techniques has been carried out along with an experimental analysis of vehicle detection using the RetinaNet framework on the VisDrone-benchmark data set. The performance of the RetinaNet framework has been validated together with the results provided by the VisDrone team. Further experiments are then conducted to investigate the impact of various parameters. Finally, the selection of suitable models that can be practically implemented is also discussed based both on a qualitative and quantitative analysis.

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge because of the limitations of current vehicle detection techniques, as well as the various camera conditions, such as height and resolution. In this work, a quadtree-based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy remain. These regions are referred to as high-accuracy identification regions (HAIRs) in this paper. We demonstrate how the use of HAIRs can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIRs in which vehicle detection accuracy is 41% higher than that in the original image extent. The use of HAIRs also significantly improves the traffic density estimation with an overall decrease of 49% in root mean squared error.

An object detection and classification method for underwater visual images based on the bag-of-words model

In this study, an autonomous underwater vehicle (AUV) detection technique was used as the background to investigate the target classification method for underwater visual images. The target classification method is based on the bag-of-words model, with reduced classification accuracy when the edges of underwater objects are blurred, and when there are many noise points. To solve this problem, an improved visual dictionary generation method was proposed based on the bag-of-words model. The proposed method includes a step on decomposing the complete contour of the target before extracting the features. The DCE (discrete contour evolution) algorithm was used to decompose the complete contour into segments of different lengths, and the SC (shape context) method was applied to extract the features of the contour segments. The FCM (Fuzzy C-Means) algorithm was then used to generate a visual dictionary. To address the classification accuracy problem that causes the underwater targets to decrease after adding spatial location information to the bag-of-words model instead of increasing, an improved spatial location information description method was proposed. The contour segments with the nearest-neighbor relationship were represented as visual phrases, constituting the spatial information of the bag-of-words model. Additionally, for the problem of the shape information of contour segments with different shapes, and the relative position information between the contour segments having different contributions to the target classification, a method was developed to balance the contributions of the two using the optimal weight selected from the experiments. The experimental results demonstrated that the proposed method can effectively improve the classification accuracy of underwater target images.

Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems.

Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle’s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is.

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.