Vehicle Detection Algorithm Research Articles

YOLOv7-RAR for Urban Vehicle Detection.

Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban roads, weak perception of small targets in perspective, and insufficient feature extraction, the YOLOv7-RAR recognition algorithm is proposed. The algorithm is improved from the following three directions based on YOLOv7. Firstly, in view of the insufficient nonlinear feature fusion of the original backbone network, the Res3Unit structure is used to reconstruct the backbone network of YOLOv7 to improve the ability of the network model architecture to obtain more nonlinear features. Secondly, in view of the problem that there are many interference backgrounds in urban roads and that the original network is weak in positioning targets such as vehicles, a plug-and-play hybrid attention mechanism module, ACmix, is added after the SPPCSPC layer of the backbone network to enhance the network's attention to vehicles and reduce the interference of other targets. Finally, aiming at the problem that the receptive field of the original network Narrows, with the deepening of the network model, leads to a high miss rate of small targets, the Gaussian receptive field scheme used in the RFLA (Gaussian-receptive-field-based label assignment) module is used at the connection between the feature fusion area and the detection head to improve the receptive field of the network model for small objects in the image. Combining the three improvement measures, the first letter of the name of each improvement measure is selected, and the improved algorithm is named the YOLOv7-RAR algorithm. Experiments show that on urban roads with crowded vehicles and different weather patterns, the average detection accuracy of the YOLOv7-RAR algorithm reaches 95.1%, which is 2.4% higher than that of the original algorithm; the AP50:90 performance is 12.6% higher than that of the original algorithm. The running speed of the YOLOv7-RAR algorithm reaches 96 FPS, which meets the real-time requirements of vehicle detection; hence, the algorithm can be better applied to vehicle detection.

Research on Vehicle Detection and Recognition Algorithm Based on Improved YOLOv5

Research on Vehicle Detection and Recognition Algorithm Based on Improved YOLOv5

Two-Stage Progressive Image Deraining Algorithm for Vehicle Detection in Rainy Days

Two-Stage Progressive Image Deraining Algorithm for Vehicle Detection in Rainy Days

3D Vehicle Detection Algorithm Based on Multimodal Decision-Level Fusion

3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving. The algorithm based on the Camera-LiDAR object candidate fusion method (CLOCs) is currently considered to be a more effective decision-level fusion algorithm, but it does not fully utilize the extracted features of 3D and 2D. Therefore, we proposed a 3D vehicle detection algorithm based on multimodal decision-level fusion. First, project the anchor point of the 3D detection bounding box into the 2D image, calculate the distance between 2D and 3D anchor points, and use this distance as a new fusion feature to enhance the feature redundancy of the network. Subsequently, add an attention module: squeeze-and-excitation networks, weight each feature channel to enhance the important features of the network, and suppress useless features. The experimental results show that the mean average precision of the algorithm in the KITTI dataset is 82.96%, which outperforms previous state-of-the-art multimodal fusion-based methods, and the average accuracy in the Easy, Moderate and Hard evaluation indicators reaches 88.96%, 82.60%, and 77.31%, respectively, which are higher compared to the original CLOCs model by 1.02%, 2.29%, and 0.41%, respectively. Compared with the original CLOCs algorithm, our algorithm has higher accuracy and better performance in 3D vehicle detection.

Realtime Vehicle Tracking Method Based on YOLOv5 + DeepSORT

In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion.

Machine Learning Techniques for Vehicle Detection

The traffic surveillance system is a type of intelligent system of traffic control. Traffic control provides solutions to most problems faced by people. It helps to monitor, detect traffic congestion and traffic accidents. As science evolved, it became possible to control traffic using video surveillance. Video surveillance is the most economical option that does not involve high costs or changes in infrastructure. Vehicle detection is one of the main parts of the traffic surveillance system. In this paper, vehicles will be detected using two different artificial intelligence methods (the YOLO method and the HAAR cascade classifier method). The first one is smarter than the second method, and both of them contain machine learning. The first processing step will read the video. Then vehicle detection algorithms are applied using two different ways. The comparison between them depends on the results to find the most effective and applicable vehicle detection method. After implementing the two methods, results were obtained using YOLO, that the accuracy is 91.31% and the error rate is 8.69%, in time 10 sec. As for using the XML (HAAR cascade classifier method) method, the accuracy is 86.9%, the quality is 86.9%, completeness is 90.9%, and the error rate is 13%, in time 17 sec. Thus, we conclude that the YOLO method has better results than the second method. Index Terms— Artificial Intelligence, Machine Learning, Vehicle Detection, Yolo Method.

A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism

Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method uses depthwise separable convolution to simplify and optimize the network, and greatly simplifies the total parameters, while the accuracy is only partially reduced. Meanwhile, a Squeeze-and-Extraction (SE) module is introduced into the backbone to improve the model′s ability to extract features; the introduction of a Convolutional Block Attention Module (CBAM) in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Furthermore, Distance-IoU (DIoU) is used to replace Intersection over Union (IoU) to calculate the regression loss for model optimization, and data augmentation technology is used to expand the dataset to achieve a better detection effect. The experimental results show that the mean Average Precision (mAP) of the proposed method reaches 82.32%, approximately 2% higher than the baseline model, while the number of parameters is only about 1/10th of that of YOLOX-S, with the size of 3.85 MB. The proposed approach is, thus, a lightweight model with high detection accuracy and suitable for various edge computing devices.

Review of Typical Vehicle Detection Algorithms Based on Deep Learning

Object detection is the crucial task in the field of computer vision. In recent years, intelligent driving technology and intelligent transportation system have set off a boom. Therefore, vehicle object detection has also become a hot research task in the field of computer vision and deep learning. With the rapid development of deep learning, the current mainstream vehicle detection algorithms are Convolutional Neural Networks (CNN)-based two-stage and one-stage object detection algorithms. Because of the local nature of the image presented by CNN, the global receptive field of the network is limited. At the same time, Transformer shows a strong long-distance dependence characteristic, and opens up a new idea of combining images with Transformer. Therefore, the research of object detection algorithm based on Transformer gradually causes a boom. This paper mainly introduces the advantages and disadvantages of several representative algorithm models, and makes a summary and prospect.

Vehicle detection algorithm based on lightweight YOLOX

Nowadays, accurate and fast vehicle detection technology is of great significance for constructing intelligent transportation systems in the context of the era of big data. This paper proposes an improved lightweight YOLOX real-time vehicle detection algorithm. Compared with the original network, the detection speed and accuracy of the new algorithm have been improved with fewer parameters. First, referring to the GhostNet, we make a lightweight design of the backbone extraction network, which significantly reduces the network parameters, training cost, and inference time. Furthermore, by introducing the α-CIoU loss function, the regression accuracy of the bounding box (bbox) is improved, while the convergence speed of the model is also accelerated. The experimental results show that the mAP of the improved algorithm on the BIT-Vehicle dataset can reach up to 99.21% with 41.2% fewer network parameters and 12.7% higher FPS than the original network and demonstrate the effectiveness of our proposed method.

Research on Vehicle Detection Algorithm Based on Improved YOLOv4

The check of vehicle fact is a significant technology in the intelligent control of transport brightness. A vehicle diagnosis and identification algorithm based on Yolov4 is proposed to encounter the dare of flow vehicle information monitoring technology in exactness, rate and firmness. The K-means++ gathering manner is used to get the mooring frame organize points appropriate for the data set. The CIOU loss function is used to optimize the training process, and then it’s trained through the CSPDarkNet53 network framework to improve the YOLOv4 network structure. The DenseNet module is applied instead of the feature pyramid. The 5-time convolution module in this paper simplifies the feature network. It is found that the experimental results have achieved good results, which can be able to achieve the goal in the practice.

Vehicle Detection From UAV Imagery With Deep Learning: A Review.

Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This crucial task needed to be done with high accuracy and speed. However, it is a very challenging task due to many characteristics related to the aerial images and the used hardware, such as different vehicle sizes, orientations, types, density, limited datasets, and inference speed. In recent years, many classical and deep-learning-based methods have been proposed in the literature to address these problems. Handed engineering- and shallow learning-based techniques suffer from poor accuracy and generalization to other complex cases. Deep-learning-based vehicle detection algorithms achieved better results due to their powerful learning ability. In this article, we provide a review on vehicle detection from UAV imagery using deep learning techniques. We start by presenting the different types of deep learning architectures, such as convolutional neural networks, recurrent neural networks, autoencoders, generative adversarial networks, and their contribution to improve the vehicle detection task. Then, we focus on investigating the different vehicle detection methods, datasets, and the encountered challenges all along with the suggested solutions. Finally, we summarize and compare the techniques used to improve vehicle detection from UAV-based images, which could be a useful aid to researchers and developers to select the most adequate method for their needs.

Real-time vehicle detection algorithm based on a lightweight You-Only-Look-Once (YOLOv5n-L) approach

A vehicle detection algorithm is of great significance for automatic driving technology. Current vehicle detection algorithms suffer from the complex structure, high configuration of hardware requirements, and the difficulty to apply to mobile terminal equipment. In order to solve these issues, this paper proposes an improved YOLOv5 algorithm, named YOLOv5n-L, for lightweight. First, a depthwise separable convolution and a C3Ghost module are used to replace several C3 modules to reduce the model parameters and improve the detection speed. Then a Squeeze-and-Excitation attention mechanism is integrated into backbone network to improve the accuracy of the algorithm and suppress the environmental interference. Finally, a bidirectional feature pyramid network is used for multi-scale feature fusion to enrich feature information and improve the feature extraction ability of the proposed algorithm. The experimental results demonstrate that compared with the original algorithm, the model weight is reduced by 40 % to only 2.3 M. The mean average precision (mAP@0.5) is increased by 1.7 %. The detection speed reaches 80 FPS, which could accurately detect vehicle targets in real-time.

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%.

Binary residual feature pyramid network: An improved feature fusion module based on double‐channel residual pyramid structure for autonomous detection algorithm

AbstractThe vehicle detection algorithm based on visual perception has been applied in all types of automatic driving scenes. However, there are still flaws in the current detection algorithm model, especially for small objects. The detection effect of vehicle objects with small pixels in the image is often missed and wrongly detected. This research proposes an improved feature fusion module based on double‐channel residual pyramid (DRP) structure for autonomous detection algorithm which named binary residual feature pyramid network (BiResFPN) to solve the above problems. Firstly, a DRP structure, which can effectively supplement the shallow information of the network, is proposed. The residual structure is added to the output feature map for further supplement. Then, an average sampling method of positive and negative samples based on intersection‐over‐union (IOU) value is proposed on the basis of this structure, aimed at the unbalanced sampling of positive and negative samples in the training stage of faster regions with CNN features (RCNN). It leads to the reduction of the interference of a large number of simple negative samples, which makes the learned model better. The experimental results based on the KITTI and BDD100K dataset datasets show that the capability of the feature fusion module based on DRP structure is strong for small object detection. Compared with Faster‐RCNN (FPN), the detection algorithm of small object detection accuracy APsmall was increased by 2.6%, APmedium and APlarge was increased by 1.1% and 0.3%.

Vehicle and Pedestrian Detection Algorithm Based on Lightweight YOLOv3-Promote and Semi-Precision Acceleration

Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model’s to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA’s TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image

YOLO-Based Efficient Vehicle Object Detection

<p>Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for accuracy and real-time. However, the existing algorithms suffer from the contradiction between detec-tion speed and detection accuracy, and weak generalization ability. To address these issues, an improved vehicle detection algorithm is presented based on the You Only Look Once (YOLO). On the one hand, an efficient feature extraction network is restructured to speed up the feature transfer of the object, and re-use the feature information extracted from the input image. On the other hand, considering that the fewer pixels are occupied for the smaller objects, a novel feature fusion network is designed to fuse the seman-tic information and representation information extracted by different depth feature extraction layers, and ultimately improve the detection accuracy of small and medium objects. Experiment results indicate that the mean Average Precision (mAP) of the proposed algorithm is up to 93.87%, which is 11.51%, 18.56% and 20.42% higher than that of YOLOv3, CornerNet, and Faster R-CNN, respectively. Furthermore, its detection speed can meet the real-time requirement of practical application basically with 49.45 frames per second.</p> <p> </p>

An Effective Approach of Vehicle Detection Using Deep Learning.

With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in which primary-stage target detection algorithms, namely, YOLOv3 algorithm and SSD algorithm, are adopted. Therefore, the method first processes the image data in the open-source road vehicle dataset for training. Then, the vehicle detection model is trained by using YOLOv3 and SSD algorithms to show the detection effect, respectively. The result is by comparing the detection effects of the two models on vehicles. The researchers accomplished the result analysis and summarized the characteristics of various models obtained by training, to apply to target tracking, semantic segmentation, and unmanned driving.

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.

Fast vehicle detection algorithm in traffic scene based on improved SSD

For autonomous driving systems, vehicle detection is an important part and challenging problem due to the complex traffic scenes and poor computing resources. This paper proposes an improved SSD(single shot mutlibox detector) algorithm for the fast detection of vehicles in traffic scenes. MobileNet v2 is selected as the backbone feature extraction network for SSD, which improves the real-time performance of the algorithm. To improve detection accuracy, the channel attention mechanism is utilized for feature weighting, and the deconvolution module is utilized to construct a bottom–top feature fusion structure. The experimental results show that the average precision of the proposed algorithm on the BDD100K and KITTI datasets is 82.59% and 84.83%, respectively. The single inference time of the algorithm is 73ms, which is only about 5/11 of the original model, realizing the improvement of inference speed and prediction accuracy.