Problem Of Vehicle Detection Research Articles

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

An Efficient Approach of Vehicle Detection Based on Deep Learning Algorithms and Wireless Sensors Networks

Machine learning is applied to analyze and classify automatically images. Artificial intelligence (AI) is considered very successful in this area. Therefore, AI is exploited to evaluate the opportunities of big data and to extract value from massive and varied data sources. In order to detect any event (person, vehicle, dog, eyes, traffic, terrorist activity), ML is explored. Hence, advanced ML techniques recur to multimedia wireless sensor networks (MWSN) to detect any event in the considered area. In this work, the authors propose an enhanced architecture MWSN, which is able fly any event detection. In this context, this paper addresses the problem of vehicle detection using convolutional neural networks using a proposed architecture MWSN. Therefore, to reach this goal, the authors assess the performance of three state-of-the-art CNN algorithms, namely faster R-CNN, which is the most popular region-based algorithm; YOLO, which is known to be the fastest one; and SSD, which takes one single shot to detect multiple objects within the image.

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.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads

• We develop a novel cleaning algorithm to remove the unnecessary features based on SIFT extractor. • An accurate object detection model is proposed by adopting the G-RCNN to process vehicle image data. • We develop an evolutionary algorithm to intelligently explore the hyper-parameters space of the IRCNN-VD. This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data , which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances.

Vehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter

Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising.

Real‐time vehicle detection and tracking using 3D LiDAR

AbstractThis paper studies the problem of vehicle detection and tracking for an autonomous vehicle using 3D Light Detection and Ranging (LiDAR). In order to increase the accuracy of vehicle detection and tracking, a new clustering algorithm is proposed to obtain vehicle candidates from preprocessed point cloud data collected by the LiDAR. A classifier trained by the support vector machine (SVM) algorithm is used to detect vehicles from vehicle candidates. Kalman filter and global nearest neighbor (GNN) algorithm are used to track vehicles, and the accuracy of vehicle detection result is further improved by the aid of tracking results. The proposed method has been verified on a testing platform.

Multisized Object Detection Using Spaceborne Optical Imagery

This article addresses the highly challenging problem of vehicle detection from high-resolution remote sensing imagery by introducing a novel medium size annotated dataset named satellite imagery multivehicles dataset (SIMD) along with an adapted single pass deep multiscale object detection framework with the aim to detect multisized/type objects for catering above-ground perspective of vehicles. The dataset images are acquired from multiple locations in the EU/US regions available in the public Google Earth satellite imagery. Specifically, it comprises 5000 images of resolution 1024 × 768 and collectively contains 45 096 objects in 15 different classes of vehicles including cars, trucks, buses, long vehicles, various types of aircrafts, and boats. In the proposed architecture, we demonstrate the relevant modifications needed to translate the state-of-the-art object detection frameworks to solve the object detection problem from remote sensing imagery. The proposed architecture has been evaluated on SIMD and a public dataset VEDAI. The comparative analysis has been performed with existing off-the-shelf single-shot object detection models including YOLO and YOLT yielding superior performance measured with standard evaluation strategies. To ignite further research in this domain, the introduced SIMD dataset and the corresponding architecture is publicly available at this link: http://vision.seecs.edu.pk/simd .

Vehicle Collision Detection During Night Time

”Vehicle collision detection during night time” is an attempt to find an improved solution for vehicle detection problem, during night time. Night time vehicle detection is a challenging area since, the contrast between back ground and foreground is very less due to non uniform lighting, reflections, multiple light sources etc. This algorithm is a blend of night time image enhancement technique and fusion technique for fusing multiple features based on their weight. Image enhancement is an important aspect of the algorithm as it helps in improving the contrast between foreground and back ground as well as improving the lighting, so that it becomes easier to distinguish vehicle and other illuminated objects such as street lights. For improved reliability of the algorithm, set of complementary features - LBP (local binary pattern), HOG (Histogram of Oriented Gradients) and a CNN based feature - are used in the algorithm and are fused together using weighted fusion technique. For classification purpose, individual SVMs are trained for each feature and another SVM is trained for final rank based vehicle detection. Accurate region of interest in computed during detection phase, and is used for vehicle detection by trained SVM classifiers. Tail light detection is used for efficient region proposals. The algorithm is improved by leveraging the region proposals and high accuracy detection of Faster RCNN [10]. The algorithm detects multiple vehicles in single image frame and at possible multiple locations and distances within the frame.

PVSS: A Progressive Vehicle Search System for Video Surveillance Networks

This paper is focused on the task of searching for a specific vehicle that appeared in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, attribute-based search cannot accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be misrecognized in surveillance scenes due to the low resolution and noise. In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules: the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of the PVSS.

Efficient moving vehicle detection for intelligent traffic surveillance system using optimal probabilistic neural network

The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.

Efficient Moving Vehicle Detection for Intelligent Traffic Surveillance System Using Optimal Probabilistic Neural Network

The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.

Vehicle Detection and Classification in Aerial Images

Background Nowadays the problem of vehicle detection and classification on aerial images received from UAV (Unmanned Aerial Vehicles) has become important because of development of UAV technology and image analysis methods. Methods The paper describes a multilevel detection of vehicles and their subsequent classification. This method can be used for search of moving and stationary vehicles. In this work we propose new approaches such as: image segmentation into superpixels by SEEDS method, trainable five-level cascade detector of combined superpixels-regions, which uses technology of artificial neutral networks. Characteristics of regions are built based on their geometric and texture features (HoG and LPB descriptors) and directly from the image patch using technology of nonlinear autoencoders. Additional cascade of the detector uses data of moving objects in the image. Similar responses of the detector are combined and classified by color and type of the vehicle. Findings For training these algorithms a largest image dataset was compiled from different sources. The results of tests of detection and classification showed high accuracy. Improvements Algorithm is fast enough to allow on-board usage. The proposed method can be applied for road traffic monitoring, analysis of parking lots occupancy and other similar tasks. Keywords: Drone, Image Dataset, Object Classification,Road Traffic Monitoring, UAV, Unmanned Aerial Vehicles Vehicle Classification,Vehicle Detection

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.

Robust vehicle tracking algorithm for nighttime videos captured by fixed cameras in highly reflective environments

In this study, the problem of vehicle detection, tracking and speed estimation in the nighttime traffic surveillance videos captured in highly reflective environments is considered. In this case, a robust algorithm is proposed which uses vehicle headlights as their prominent features. The proposed algorithm consists of three main stages. In the first stage, bright objects are segmented by thresholding the grey‐scale image. An effective algorithm is then applied to distinguish between vehicles lights and lights reflected on the road and on the vehicles bodies. In the second stage, the segmented bright objects are tracked using their spatial characteristics and their shapes and then, their speeds are estimated. To correct the camera perspective effect and reduce computational complexity, a projective transformation is used. In the third stage, the lights of each vehicle are grouped and paired using their positions and speeds. Motorbikes are also identified among the unpaired lights in this stage. Finally, the proposed real‐time system is implemented in C and applied to videos captured by traffic surveillance cameras in some highways in Iran. Experimental results reveal that accuracy of the algorithm proposed for vehicle detection is more than 98%.

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

VEHICLE DETECTION FROM AN IMAGE SEQUENCE COLLECTED BY A HOVERING HELICOPTER

Abstract. This paper addresses the problem of vehicle detection from an image sequence in difficult cases. Difficulties are notably caused by relatively small vehicles, vehicles that appear with low contrast or vehicles that drive at low speed. The image sequence considered here is recorded by a hovering helicopter and was stabilized prior to the vehicle detection step considered here. A practical algorithm is designed and implemented for this purpose of vehicle detection. Each pixel is identified firstly as either a background (road) or a foreground (vehicle) pixel by analyzing its gray-level temporal profile in a sequential way. Secondly, a vehicle is identified as a cluster of foreground pixels. The results of this new method are demonstrated on a test image-sequence featuring very congested traffic but also smoothly flowing traffic. It is shown that for both traffic situations the method is able to successfully detect low contrast, small size and low speed vehicles.

Adaptive Vehicle Detector Approach for Complex Environments

In this paper, a vehicle detection approach for complex environments is presented. This paper proposes methods for solving problems of vehicle detection in traffic jams and complex weather conditions such as sunny days, rainy days, cloudy days, sunrise time, sunset time, or nighttime. In recent research, there have been many well-known vehicle detectors that utilize background extraction methods to recognize vehicles. In these studies, the background image needs to continuously be updated; otherwise, the luminance variation will impact the detection quality. The vehicle detection under various environments will have many difficulties such as illumination vibrations, shadow effects, and vehicle overlapping problems that appear in traffic jams. The main contribution of this paper is to propose an adaptive vehicle detection approach in complex environments to directly detect vehicles without extracting and updating a reference background image in complex environments. In the proposed approach, histogram extension addresses the removal of the effects of weather and light impact. The gray-level differential value method is utilized to directly extract moving objects from the images. Finally, tracking and error compensation are applied to refine the target tracking quality. In addition, many useful traffic parameters are evaluated. These useful traffic parameters, including traffic flows, velocity, and vehicle classifications, can help to control traffic and provide drivers with good guidance. Experimental results show that the proposed methods are robust, accurate, and powerful enough to overcome complex weather conditions and traffic jams.

Visual Detection of Moving Vehicles Ahead Based on the Characteristics

Machine vision perception technology is widely used in the vehicle’s active safety system. It provides more immediate and correct information of road and vehicles around, in which inspection of moving vehicle ahead is one of the important items. A method of inspection fused of detection of the shadow under the vehicle and symmetry of the vehicle’s tail is presented in this paper. At first, a region of interest is selected according to the lane lines. Then, the shadow can be detected with grayscale histogram in the region of interest and a suspected area of vehicle is obtained by expanding the shadow with empirical proportion. At last, the vehicle ahead is further affirmed by calculating the symmetry of such characteristic at its tail as grayscale value, taillight and the edges. Experimental results prove that this method can well solve the actual problems of vehicle detection.

Classification-based vehicle detection in high-resolution satellite images

In this study, we have looked into the problem of vehicle detection in high-resolution satellite images. Based on the input from the local road authorities, we have focused not only on highways, but also on inner city roads, where more clutter is expected. The study site is the city of Oslo, Norway. To do vehicle detection in these areas, we propose an automatic approach, consisting of a segmentation step, followed by two stages of object classification. In the process, we utilize multispectral images, panchromatic images and a road network. The approach has been tested on Quickbird images, and the results that are obtained have been compared with manual counts and classifications.

A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection

We present an algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results of the generative (HoG features), discriminative (Haar-like features) detectors, and of their fusion is presented. These results show that the fusion combines the advantages of the other two detectors: generative classifiers eliminate "easily" negative examples in the early layers of the cascade, while in the later layers, the discriminative classifiers generate a fine decision boundary removing the negative examples near the vehicle model. The best algorithm achieves good performances on a test set containing some 500 vehicle images: the detection rate is about 94% and the false-alarm rate per image is 0.0003.